diff --git a/LICENSE b/LICENSE
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--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
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+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) <year>  <name of author>
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<http://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<http://www.gnu.org/philosophy/why-not-lgpl.html>.
\ No newline at end of file
diff --git a/README b/README
new file mode 100644
index 0000000..7cc233e
--- /dev/null
+++ b/README
@@ -0,0 +1,35 @@
+This will be a C++ implementations of the OpenTLD (aka Predator)
+----------------------------------------------------------------------------
+This is a work in progress, as of right now the code is functional but pretty slow.
+----------------------------------------------------------------------------
+Installation Notes
+=====================================
+git clone git@github.com:alantrrs/OpenTLD.git
+cd OpenTLD
+mkdir build
+cd build
+cmake ../src/
+make
+cd ../bin/
+%To run from camera
+./run_tld -p ../parameters.yml
+%To run from file
+./run_tld -p ../parameters.yml -s ../datasets/06_car/car.mpg
+%To init bounding box from file
+./run_tld -p ../parameters.yml -s ../datasets/06_car/car.mpg -b ../datasets/06_car/init.txt
+%To train only in the firs frame (no tracking, no learning)
+./run_tld -p ../parameters.yml -s ../datasets/06_car/car.mpg -b ../datasets/06_car/init.txt -no_tl 
+%To test the final detector (Repeat the video, first time learns, second time detects)
+./run_tld -p ../parameters.yml -s ../datasets/06_car/car.mpg -b ../datasets/06_car/init.txt -r
+
+=====================================
+Evaluation
+=====================================
+The output of the program is a file called bounding_boxes.txt which contains all the detections made through the video. This file should be compared with the ground truth file to evaluate the performance of the algorithm. This is done using a python script:
+python ../datasets/evaluate_vis.py ../datasets/06_car/car.mpg bounding_boxes.txt ../datasets/06_car/gt.txt
+
+====================================
+Thanks
+====================================
+To Zdenek Kalal for realeasing his awesome algorithm
+
diff --git a/README.md b/README.md
index f92231c..4d92ef3 100644
--- a/README.md
+++ b/README.md
@@ -3,11 +3,18 @@ from https://github.com/alantrrs/OpenTLD
 
 # Windows
 
-  checkout branch/windows
-  opencv:2.4.5
+	$ git clone git@github.com:kyokyojiang/OpenTLD.git
+  	$ git branch origin/windows
+	$ git pull
 
+# Ubuntu
 
-# Ubuntu12.04
-
-  checkout branch/ubuntu
-  opencv:2.4.12.1
+	$ emacs /etc/profile
+	  # add
+	  OpenCV_DIR=/home/install/opencv2.4.12.1
+	  LD_LIBRARY_PATH=$OpenCV_DIR/lib:$LD_LIBRARY_PATH
+	  export OpenCV_DIR LD_LIBRARY_PATH
+	$ source /etc/profile
+	$ git clone git@github.com:kyokyojiang/OpenTLD.git
+	$ git branch origin/ubuntu
+	$ git pull
diff --git a/datasets/01_david/david.mpg b/datasets/01_david/david.mpg
new file mode 100644
index 0000000..e4580ec
Binary files /dev/null and b/datasets/01_david/david.mpg differ
diff --git a/datasets/01_david/gt.txt b/datasets/01_david/gt.txt
new file mode 100644
index 0000000..fc72a07
--- /dev/null
+++ b/datasets/01_david/gt.txt
@@ -0,0 +1,761 @@
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diff --git a/datasets/01_david/init.txt b/datasets/01_david/init.txt
new file mode 100644
index 0000000..99d30bb
--- /dev/null
+++ b/datasets/01_david/init.txt
@@ -0,0 +1 @@
+165,93,216,147
\ No newline at end of file
diff --git a/datasets/01_david/pn_tracker.txt b/datasets/01_david/pn_tracker.txt
new file mode 100644
index 0000000..1ea9dda
--- /dev/null
+++ b/datasets/01_david/pn_tracker.txt
@@ -0,0 +1,760 @@
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diff --git a/datasets/02_jumping/gt.txt b/datasets/02_jumping/gt.txt
new file mode 100644
index 0000000..12077be
--- /dev/null
+++ b/datasets/02_jumping/gt.txt
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diff --git a/datasets/02_jumping/init.txt b/datasets/02_jumping/init.txt
new file mode 100644
index 0000000..e673894
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diff --git a/datasets/02_jumping/jumping.mpg b/datasets/02_jumping/jumping.mpg
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index 0000000..a490b45
Binary files /dev/null and b/datasets/02_jumping/jumping.mpg differ
diff --git a/datasets/02_jumping/pn_tracker.txt b/datasets/02_jumping/pn_tracker.txt
new file mode 100644
index 0000000..b8ef913
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new file mode 100644
index 0000000..9fa3b0e
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diff --git a/datasets/03_pedestrian1/gt.txt b/datasets/03_pedestrian1/gt.txt
new file mode 100644
index 0000000..70d99c8
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diff --git a/datasets/03_pedestrian1/init.txt b/datasets/03_pedestrian1/init.txt
new file mode 100644
index 0000000..f9e3532
--- /dev/null
+++ b/datasets/03_pedestrian1/init.txt
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diff --git a/datasets/03_pedestrian1/pedestrian1.mpg b/datasets/03_pedestrian1/pedestrian1.mpg
new file mode 100644
index 0000000..a226dd5
Binary files /dev/null and b/datasets/03_pedestrian1/pedestrian1.mpg differ
diff --git a/datasets/03_pedestrian1/pn_tracker.txt b/datasets/03_pedestrian1/pn_tracker.txt
new file mode 100644
index 0000000..361f8d2
--- /dev/null
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new file mode 100644
index 0000000..2c3f36c
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diff --git a/datasets/04_pedestrian2/CVPR.txt b/datasets/04_pedestrian2/CVPR.txt
new file mode 100644
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diff --git a/datasets/04_pedestrian2/MIL.original.txt b/datasets/04_pedestrian2/MIL.original.txt
new file mode 100644
index 0000000..f6e9a2d
--- /dev/null
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diff --git a/datasets/04_pedestrian2/MIL.txt b/datasets/04_pedestrian2/MIL.txt
new file mode 100644
index 0000000..078106b
--- /dev/null
+++ b/datasets/04_pedestrian2/MIL.txt
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diff --git a/datasets/04_pedestrian2/OnlineBoost.txt b/datasets/04_pedestrian2/OnlineBoost.txt
new file mode 100644
index 0000000..664b7b3
--- /dev/null
+++ b/datasets/04_pedestrian2/OnlineBoost.txt
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diff --git a/datasets/04_pedestrian2/SemiBoost.txt b/datasets/04_pedestrian2/SemiBoost.txt
new file mode 100644
index 0000000..7e95ce5
--- /dev/null
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diff --git a/datasets/04_pedestrian2/TLD1.0.txt b/datasets/04_pedestrian2/TLD1.0.txt
new file mode 100644
index 0000000..4ead993
--- /dev/null
+++ b/datasets/04_pedestrian2/TLD1.0.txt
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diff --git a/datasets/04_pedestrian2/coGD.original.txt b/datasets/04_pedestrian2/coGD.original.txt
new file mode 100644
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diff --git a/datasets/04_pedestrian2/coGD.txt b/datasets/04_pedestrian2/coGD.txt
new file mode 100644
index 0000000..8d045bc
--- /dev/null
+++ b/datasets/04_pedestrian2/coGD.txt
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new file mode 100644
index 0000000..a32e624
--- /dev/null
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new file mode 100644
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diff --git a/datasets/04_pedestrian2/init.txt b/datasets/04_pedestrian2/init.txt
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diff --git a/datasets/04_pedestrian2/initial_detector.txt b/datasets/04_pedestrian2/initial_detector.txt
new file mode 100644
index 0000000..c1c5ccc
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diff --git a/datasets/04_pedestrian2/pedestrian2.mpg b/datasets/04_pedestrian2/pedestrian2.mpg
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diff --git a/datasets/04_pedestrian2/pn_tracker.txt b/datasets/04_pedestrian2/pn_tracker.txt
new file mode 100644
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diff --git a/datasets/05_pedestrian3/BeyondSemiBoost.txt b/datasets/05_pedestrian3/BeyondSemiBoost.txt
new file mode 100644
index 0000000..72fc56e
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new file mode 100644
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diff --git a/datasets/05_pedestrian3/MIL.original.txt b/datasets/05_pedestrian3/MIL.original.txt
new file mode 100644
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--- /dev/null
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new file mode 100644
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new file mode 100644
index 0000000..ce447a4
--- /dev/null
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new file mode 100644
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new file mode 100644
index 0000000..01bd4a9
--- /dev/null
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new file mode 100644
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diff --git a/datasets/05_pedestrian3/final_detector.txt b/datasets/05_pedestrian3/final_detector.txt
new file mode 100644
index 0000000..1b40cd3
--- /dev/null
+++ b/datasets/05_pedestrian3/final_detector.txt
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diff --git a/datasets/05_pedestrian3/gt.txt b/datasets/05_pedestrian3/gt.txt
new file mode 100644
index 0000000..b2cd549
--- /dev/null
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diff --git a/datasets/05_pedestrian3/init.txt b/datasets/05_pedestrian3/init.txt
new file mode 100644
index 0000000..586a165
--- /dev/null
+++ b/datasets/05_pedestrian3/init.txt
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/datasets/05_pedestrian3/initial_detector.txt b/datasets/05_pedestrian3/initial_detector.txt
new file mode 100644
index 0000000..02b6217
--- /dev/null
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diff --git a/datasets/05_pedestrian3/pedestrian3.mpg b/datasets/05_pedestrian3/pedestrian3.mpg
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index 0000000..d2ae894
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diff --git a/datasets/05_pedestrian3/pn_tracker.txt b/datasets/05_pedestrian3/pn_tracker.txt
new file mode 100644
index 0000000..f93f575
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diff --git a/datasets/06_car/BeyondSemiBoost.txt b/datasets/06_car/BeyondSemiBoost.txt
new file mode 100644
index 0000000..21231ba
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new file mode 100644
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diff --git a/datasets/06_car/MIL.original.txt b/datasets/06_car/MIL.original.txt
new file mode 100644
index 0000000..d8fcc91
--- /dev/null
+++ b/datasets/06_car/MIL.original.txt
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diff --git a/datasets/06_car/MIL.txt b/datasets/06_car/MIL.txt
new file mode 100644
index 0000000..a155ea3
--- /dev/null
+++ b/datasets/06_car/MIL.txt
@@ -0,0 +1,945 @@
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diff --git a/datasets/06_car/OnlineBoost.txt b/datasets/06_car/OnlineBoost.txt
new file mode 100644
index 0000000..866ea1b
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diff --git a/datasets/06_car/SemiBoost.txt b/datasets/06_car/SemiBoost.txt
new file mode 100644
index 0000000..df61005
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diff --git a/datasets/06_car/TLD1.0.txt b/datasets/06_car/TLD1.0.txt
new file mode 100644
index 0000000..38a089a
--- /dev/null
+++ b/datasets/06_car/TLD1.0.txt
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diff --git a/datasets/06_car/car.mpg b/datasets/06_car/car.mpg
new file mode 100644
index 0000000..b152a2e
Binary files /dev/null and b/datasets/06_car/car.mpg differ
diff --git a/datasets/06_car/coGD.original.txt b/datasets/06_car/coGD.original.txt
new file mode 100644
index 0000000..9d0c210
--- /dev/null
+++ b/datasets/06_car/coGD.original.txt
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diff --git a/datasets/06_car/coGD.txt b/datasets/06_car/coGD.txt
new file mode 100644
index 0000000..65dba81
--- /dev/null
+++ b/datasets/06_car/coGD.txt
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diff --git a/datasets/06_car/final_detector.txt b/datasets/06_car/final_detector.txt
new file mode 100644
index 0000000..0d03d23
--- /dev/null
+++ b/datasets/06_car/final_detector.txt
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diff --git a/datasets/06_car/gt.txt b/datasets/06_car/gt.txt
new file mode 100644
index 0000000..982bfbe
--- /dev/null
+++ b/datasets/06_car/gt.txt
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diff --git a/datasets/06_car/init.txt b/datasets/06_car/init.txt
new file mode 100644
index 0000000..c944f94
--- /dev/null
+++ b/datasets/06_car/init.txt
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\ No newline at end of file
diff --git a/datasets/06_car/initial_detector.txt b/datasets/06_car/initial_detector.txt
new file mode 100644
index 0000000..f26fa76
--- /dev/null
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new file mode 100644
index 0000000..589a926
--- /dev/null
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diff --git a/datasets/07_motocross/BeyondSemiBoost.txt b/datasets/07_motocross/BeyondSemiBoost.txt
new file mode 100644
index 0000000..2df0ac9
--- /dev/null
+++ b/datasets/07_motocross/BeyondSemiBoost.txt
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diff --git a/datasets/07_motocross/CVPR.txt b/datasets/07_motocross/CVPR.txt
new file mode 100644
index 0000000..a6de4a1
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diff --git a/datasets/07_motocross/MIL.txt b/datasets/07_motocross/MIL.txt
new file mode 100644
index 0000000..63fddbb
--- /dev/null
+++ b/datasets/07_motocross/MIL.txt
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diff --git a/datasets/07_motocross/OnlineBoost.txt b/datasets/07_motocross/OnlineBoost.txt
new file mode 100644
index 0000000..f5b7083
--- /dev/null
+++ b/datasets/07_motocross/OnlineBoost.txt
@@ -0,0 +1,15 @@
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diff --git a/datasets/07_motocross/SemiBoost.txt b/datasets/07_motocross/SemiBoost.txt
new file mode 100644
index 0000000..ee3814f
--- /dev/null
+++ b/datasets/07_motocross/SemiBoost.txt
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diff --git a/datasets/07_motocross/TLD1.0.txt b/datasets/07_motocross/TLD1.0.txt
new file mode 100644
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diff --git a/datasets/07_motocross/gt.txt b/datasets/07_motocross/gt.txt
new file mode 100644
index 0000000..900911d
--- /dev/null
+++ b/datasets/07_motocross/gt.txt
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new file mode 100644
index 0000000..372f276
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diff --git a/datasets/08_volkswagen/BeyondSemiBoost.txt b/datasets/08_volkswagen/BeyondSemiBoost.txt
new file mode 100644
index 0000000..4e1810e
--- /dev/null
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new file mode 100644
index 0000000..5d46c46
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diff --git a/datasets/08_volkswagen/MIL.original.txt b/datasets/08_volkswagen/MIL.original.txt
new file mode 100644
index 0000000..673c83f
--- /dev/null
+++ b/datasets/08_volkswagen/MIL.original.txt
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diff --git a/datasets/08_volkswagen/MIL.txt b/datasets/08_volkswagen/MIL.txt
new file mode 100644
index 0000000..b103838
--- /dev/null
+++ b/datasets/08_volkswagen/MIL.txt
@@ -0,0 +1,501 @@
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diff --git a/datasets/08_volkswagen/OnlineBoost.txt b/datasets/08_volkswagen/OnlineBoost.txt
new file mode 100644
index 0000000..7fbd488
--- /dev/null
+++ b/datasets/08_volkswagen/OnlineBoost.txt
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diff --git a/datasets/08_volkswagen/SemiBoost.txt b/datasets/08_volkswagen/SemiBoost.txt
new file mode 100644
index 0000000..2f93562
--- /dev/null
+++ b/datasets/08_volkswagen/SemiBoost.txt
@@ -0,0 +1,8575 @@
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diff --git a/datasets/08_volkswagen/TLD1.0.txt b/datasets/08_volkswagen/TLD1.0.txt
new file mode 100644
index 0000000..4f1e912
--- /dev/null
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diff --git a/datasets/08_volkswagen/coGD.txt b/datasets/08_volkswagen/coGD.txt
new file mode 100644
index 0000000..1092251
--- /dev/null
+++ b/datasets/08_volkswagen/coGD.txt
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diff --git a/datasets/08_volkswagen/gt.txt b/datasets/08_volkswagen/gt.txt
new file mode 100644
index 0000000..247632d
--- /dev/null
+++ b/datasets/08_volkswagen/gt.txt
@@ -0,0 +1,8576 @@
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diff --git a/datasets/08_volkswagen/init.txt b/datasets/08_volkswagen/init.txt
new file mode 100644
index 0000000..acf31e9
--- /dev/null
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diff --git a/datasets/08_volkswagen/initial_detector.txt b/datasets/08_volkswagen/initial_detector.txt
new file mode 100644
index 0000000..1a69474
--- /dev/null
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diff --git a/datasets/09_carchase/BeyondSemiBoost.txt b/datasets/09_carchase/BeyondSemiBoost.txt
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diff --git a/datasets/09_carchase/CVPR.txt b/datasets/09_carchase/CVPR.txt
new file mode 100644
index 0000000..eae7bd8
--- /dev/null
+++ b/datasets/09_carchase/CVPR.txt
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diff --git a/datasets/09_carchase/MIL.original.txt b/datasets/09_carchase/MIL.original.txt
new file mode 100644
index 0000000..63e6403
--- /dev/null
+++ b/datasets/09_carchase/MIL.original.txt
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diff --git a/datasets/09_carchase/MIL.txt b/datasets/09_carchase/MIL.txt
new file mode 100644
index 0000000..e5954e2
--- /dev/null
+++ b/datasets/09_carchase/MIL.txt
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diff --git a/datasets/09_carchase/OnlineBoost.txt b/datasets/09_carchase/OnlineBoost.txt
new file mode 100644
index 0000000..3e01c5f
--- /dev/null
+++ b/datasets/09_carchase/OnlineBoost.txt
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diff --git a/datasets/09_carchase/SemiBoost.txt b/datasets/09_carchase/SemiBoost.txt
new file mode 100644
index 0000000..8014fce
--- /dev/null
+++ b/datasets/09_carchase/SemiBoost.txt
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diff --git a/datasets/09_carchase/TLD1.0.txt b/datasets/09_carchase/TLD1.0.txt
new file mode 100644
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diff --git a/datasets/09_carchase/gt.txt b/datasets/09_carchase/gt.txt
new file mode 100644
index 0000000..f7e297e
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diff --git a/datasets/10_panda/CVPR.txt b/datasets/10_panda/CVPR.txt
new file mode 100644
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diff --git a/datasets/10_panda/MIL.originaloriginal.txt b/datasets/10_panda/MIL.originaloriginal.txt
new file mode 100644
index 0000000..fb6025e
--- /dev/null
+++ b/datasets/10_panda/MIL.originaloriginal.txt
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diff --git a/datasets/10_panda/MIL.txt b/datasets/10_panda/MIL.txt
new file mode 100644
index 0000000..9d35e9e
--- /dev/null
+++ b/datasets/10_panda/MIL.txt
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diff --git a/datasets/10_panda/OnlineBoost.txt b/datasets/10_panda/OnlineBoost.txt
new file mode 100644
index 0000000..11ff2f6
--- /dev/null
+++ b/datasets/10_panda/OnlineBoost.txt
@@ -0,0 +1,1004 @@
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diff --git a/datasets/10_panda/SemiBoost.txt b/datasets/10_panda/SemiBoost.txt
new file mode 100644
index 0000000..13fec4b
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diff --git a/datasets/10_panda/coGD.txt b/datasets/10_panda/coGD.txt
new file mode 100644
index 0000000..7ae052f
--- /dev/null
+++ b/datasets/10_panda/coGD.txt
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diff --git a/datasets/10_panda/gt.txt b/datasets/10_panda/gt.txt
new file mode 100644
index 0000000..a367250
--- /dev/null
+++ b/datasets/10_panda/gt.txt
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diff --git a/datasets/10_panda/init.txt b/datasets/10_panda/init.txt
new file mode 100644
index 0000000..c2971cd
--- /dev/null
+++ b/datasets/10_panda/init.txt
@@ -0,0 +1 @@
+58,100,85,122
\ No newline at end of file
diff --git a/datasets/10_panda/panda.mpg b/datasets/10_panda/panda.mpg
new file mode 100644
index 0000000..7f38dd4
Binary files /dev/null and b/datasets/10_panda/panda.mpg differ
diff --git a/datasets/evaluate_all_visual.sh b/datasets/evaluate_all_visual.sh
new file mode 100755
index 0000000..8360ef5
--- /dev/null
+++ b/datasets/evaluate_all_visual.sh
@@ -0,0 +1,14 @@
+#!/bin/bash
+datasets="*"
+gt_file="gt.txt"
+evaluation_file="evaluation_all_visual.txt"
+for dir in $datasets; do
+    if [ -d $dir ]; then
+        if [ -f "$dir/$1" ]; then
+			source=$(ls $dir | grep .mpg)
+        	echo "[$dir]" >> $evaluation_file	
+			python evaluate_vis.py "$dir/$source" "$dir/$1" "$dir/$gt_file" >> $evaluation_file
+			#mv evaluation.avi $dir
+		fi
+    fi
+done
diff --git a/datasets/evaluate_vis.py b/datasets/evaluate_vis.py
new file mode 100755
index 0000000..179c43d
--- /dev/null
+++ b/datasets/evaluate_vis.py
@@ -0,0 +1,69 @@
+import csv
+import sys
+import cv
+import cv2
+def bb_overlap(bb1,bb2,frame):
+	if (bb1[0] == 'NaN' or bb2[0] == 'NaN'):
+		return 0
+	if (bb1[0]>bb2[2]):
+		return 0
+	if (bb1[1]>bb2[3]):
+		return 0
+	if (bb1[2]<bb2[0]):
+		return 0
+	if (bb1[3]<bb2[1]):
+		return 0
+	x1 = max(bb1[0],bb2[0])
+	x2 = min(bb1[2],bb2[2])
+	y1 = max(bb1[1],bb2[1])
+	y2 = min(bb1[3],bb2[3])
+	colInt = x2-x1
+	rowInt = y2-y1
+	frame[y1:y2,x1:x2]=(100,200,100)
+	intersection = colInt*rowInt
+	area1 = (bb1[2]-bb1[0])*(bb1[3]-bb1[1])
+	area2 = (bb2[2]-bb2[0])*(bb2[3]-bb2[1])
+	overlap = intersection/(area1 + area2 - intersection)
+	#if (overlap<0.5):
+	#	print "overlap %f" %overlap
+	return overlap
+def drawBox(frame,box,color):
+	cv2.rectangle(frame,(int(box[0]),int(box[1])),(int(box[2]),int(box[3])),color,1)
+	
+cap = cv2.VideoCapture(sys.argv[1])
+#size = (int(cap.get(cv.CV_CAP_PROP_FRAME_WIDTH)),int(cap.get(cv.CV_CAP_PROP_FRAME_HEIGHT)))
+#rec = cv2.VideoWriter("evaluation.avi",cv.CV_FOURCC('P','I','M','1'),30,size,1)
+boxes = csv.reader(open(sys.argv[2],'rb'),delimiter=',')
+true_boxes = csv.reader(open(sys.argv[3],'rb'),delimiter=',')
+correct_detections = 0.0
+detections = 0.0
+true_detections = 0.0
+
+while (1):
+	ret,frame= cap.read()
+	if (ret!=1):
+		break
+	true_box = true_boxes.next()
+	box = boxes.next()
+	if (box[0] != 'NaN'):
+		box = map(float,box)
+		drawBox(frame,box,(0,0,255))
+		detections=detections+1
+	if (true_box[0] != 'NaN'):
+		true_detections=true_detections +1
+		true_box = map(float,true_box)
+		drawBox(frame,true_box,(0,255,0))
+	if (bb_overlap(box,true_box,frame)>0.25):
+		correct_detections = correct_detections + 1
+	cv2.imshow("Evaluation",frame)
+	#rec.write(frame)
+	cv2.waitKey(30)
+print "detections = %f" % detections
+print "true detections = %f" % true_detections
+print "correct detections = %f" % correct_detections
+precision = correct_detections/detections
+recall = correct_detections/true_detections
+f_measure =  2*precision*recall/(precision+recall) if (precision >0 or recall >0) else 0
+print "precision=%f" %  precision
+print "recall=%f" % recall
+print "f-measure= %f" % f_measure
diff --git a/datasets/minbox.py b/datasets/minbox.py
new file mode 100755
index 0000000..ac3b21a
--- /dev/null
+++ b/datasets/minbox.py
@@ -0,0 +1,16 @@
+import csv
+import sys
+import cv2
+
+def box_size(b):
+	return min(b[2]-b[0],b[3]-b[1])
+	
+boxes = csv.reader(open(sys.argv[1],'rb'),delimiter=',')
+min_box=1000000
+for box in boxes:
+	if (box[0] != 'NaN'):
+		box = map(float,box)
+		if (box_size(box) < min_box):
+			min_box = box_size(box)
+	
+print min_box
diff --git a/datasets/readme.pdf b/datasets/readme.pdf
new file mode 100644
index 0000000..6d74ce3
Binary files /dev/null and b/datasets/readme.pdf differ
diff --git a/doc/cpp_call_graph.dot b/doc/cpp_call_graph.dot
new file mode 100644
index 0000000..cdcc597
--- /dev/null
+++ b/doc/cpp_call_graph.dot
@@ -0,0 +1,85 @@
+digraph graphname {
+ranksep = 1.5;
+"FerNNClassifier::trainF";
+"FerNNClassifier::update";
+"FerNNClassifier::trainNN";
+"FerNNClassifier::NNConf";
+"FerNNClassifier::evaluateTh";
+"TLD::init";
+"TLD::buildGrid";
+"TLD::getOverlappingBoxes";
+"FerNNClassifier::prepare";
+"TLD::generatePositiveData";
+"TLD::generateNegativeData";
+"FerNNClassifier::trainF";
+"FerNNClassifier::trainNN";
+"FerNNClassifier::evaluateTh";
+"TLD::generatePositiveData";
+"TLD::getPattern";
+"FerNNClassifier::getFeatures";
+"TLD::generateNegativeData";
+"TLD::processFrame";
+"TLD::track";
+"TLD::detect";
+"TLD::clusterConf";
+"TLD::learn";
+"TLD::track";
+"TLD::bbPoints";
+"LKTracker::trackf2f";
+"TLD::bbPredict";
+"TLD::detect";
+"FerNNClassifier::getNumStructs";
+"FerNNClassifier::getFernTh";
+"FerNNClassifier::measure_forest";
+"FerNNClassifier::getNNTh";
+"TLD::learn";
+"FerNNClassifier::show";
+"TLD::getOverlappingBoxes";
+"TLD::getBBHull";
+main;
+"TLD::read";
+"TLD::init";
+"TLD::processFrame";
+"FerNNClassifier::trainF"->"FerNNClassifier::update";
+"FerNNClassifier::trainNN"->"FerNNClassifier::NNConf";
+"FerNNClassifier::evaluateTh"->"FerNNClassifier::NNConf";
+"TLD::init"->"TLD::buildGrid";
+"TLD::init"->"TLD::getOverlappingBoxes";
+"TLD::init"->"FerNNClassifier::prepare";
+"TLD::init"->"TLD::generatePositiveData";
+"TLD::init"->"TLD::generateNegativeData";
+"TLD::init"->"FerNNClassifier::trainF";
+"TLD::init"->"FerNNClassifier::trainNN";
+"TLD::init"->"FerNNClassifier::evaluateTh";
+"TLD::generatePositiveData"->"TLD::getPattern";
+"TLD::generatePositiveData"->"FerNNClassifier::getFeatures";
+"TLD::generateNegativeData"->"FerNNClassifier::getFeatures";
+"TLD::generateNegativeData"->"TLD::getPattern";
+"TLD::processFrame"->"TLD::track";
+"TLD::processFrame"->"TLD::detect";
+"TLD::processFrame"->"TLD::clusterConf";
+"TLD::processFrame"->"TLD::learn";
+"TLD::track"->"TLD::bbPoints";
+"TLD::track"->"LKTracker::trackf2f";
+"TLD::track"->"TLD::bbPredict";
+"TLD::track"->"TLD::getPattern";
+"TLD::track"->"FerNNClassifier::NNConf";
+"TLD::detect"->"FerNNClassifier::getNumStructs";
+"TLD::detect"->"FerNNClassifier::getFernTh";
+"TLD::detect"->"FerNNClassifier::getFeatures";
+"TLD::detect"->"FerNNClassifier::measure_forest";
+"TLD::detect"->"FerNNClassifier::getNNTh";
+"TLD::detect"->"TLD::getPattern";
+"TLD::detect"->"FerNNClassifier::NNConf";
+"TLD::learn"->"TLD::getPattern";
+"TLD::learn"->"FerNNClassifier::NNConf";
+"TLD::learn"->"TLD::getOverlappingBoxes";
+"TLD::learn"->"TLD::generatePositiveData";
+"TLD::learn"->"FerNNClassifier::trainF";
+"TLD::learn"->"FerNNClassifier::trainNN";
+"TLD::learn"->"FerNNClassifier::show";
+"TLD::getOverlappingBoxes"->"TLD::getBBHull";
+"main"->"TLD::read";
+"main"->"TLD::init";
+"main"->"TLD::processFrame";
+}
diff --git a/doc/cpp_port.png b/doc/cpp_port.png
new file mode 100644
index 0000000..59ef32d
Binary files /dev/null and b/doc/cpp_port.png differ
diff --git a/doc/dependency.png b/doc/dependency.png
new file mode 100644
index 0000000..2a2069b
Binary files /dev/null and b/doc/dependency.png differ
diff --git a/doc/grapher.py b/doc/grapher.py
new file mode 100755
index 0000000..98db9a1
--- /dev/null
+++ b/doc/grapher.py
@@ -0,0 +1,49 @@
+#!/usr/bin/env python
+import os
+import sys
+import re
+# Impor graphviz
+import gv
+
+# Import pygraph
+from pygraph.classes.graph import graph
+from pygraph.classes.digraph import digraph
+from pygraph.algorithms.searching import depth_first_search
+from pygraph.readwrite.dot import write
+
+# set a directory
+dir = sys.argv[1]
+# set the file extensions to look for '.m' and '.mex'
+exts=['.m','.mex']
+funcList = []
+fileList = []
+for root, subFolders, files in os.walk(dir):
+   for file in files:
+     fileExt = os.path.splitext(file)[-1]
+     for ext in exts:
+        if (fileExt == ext):
+          funcList.append(os.path.splitext(file)[0])
+          fileList.append(os.path.join(root,file))
+#print fileList
+#print funcList
+
+# Graph creation
+gr = digraph()
+# Add nodes
+gr.add_nodes(funcList)
+#Add Edges
+i=0
+for file in fileList:
+  f = open(file,'r')
+  fbuff = f.read()
+  for func in funcList:
+    if (func in fbuff and func != funcList[i]):
+      gr.add_edge((funcList[i],func))
+  i=i+1
+  fbuff = ""
+
+# Draw as PNG
+dot = write(gr)
+gvv = gv.readstring(dot)
+gv.layout(gvv,'dot')
+gv.render(gvv,'png','dependency.png')
diff --git a/doc/processframe.dot b/doc/processframe.dot
new file mode 100644
index 0000000..d9095be
--- /dev/null
+++ b/doc/processframe.dot
@@ -0,0 +1,32 @@
+digraph graphname {
+ranksep = 1.5;
+"FerNNClassifier::trainF"->"FerNNClassifier::update";
+"FerNNClassifier::trainNN"->"FerNNClassifier::NNConf";
+"TLD::generatePositiveData"->"TLD::getPattern";
+"TLD::generatePositiveData"->"FerNNClassifier::getFeatures";
+"TLD::processFrame"->"TLD::track";
+"TLD::processFrame"->"TLD::detect";
+"TLD::processFrame"->"TLD::clusterConf";
+"TLD::processFrame"->"TLD::learn";
+"TLD::track"->"TLD::bbPoints";
+"TLD::track"->"LKTracker::trackf2f";
+"TLD::track"->"TLD::bbPredict";
+"TLD::track"->"TLD::getPattern";
+"TLD::track"->"FerNNClassifier::NNConf";
+"TLD::detect"->"FerNNClassifier::getNumStructs";
+"TLD::detect"->"FerNNClassifier::getFernTh";
+"TLD::detect"->"FerNNClassifier::getFeatures";
+"TLD::detect"->"FerNNClassifier::measure_forest";
+"TLD::detect"->"FerNNClassifier::getNNTh";
+"TLD::detect"->"TLD::getPattern";
+"TLD::detect"->"FerNNClassifier::NNConf";
+"TLD::learn"->"TLD::getPattern";
+"TLD::learn"->"FerNNClassifier::NNConf";
+"TLD::learn"->"TLD::getOverlappingBoxes";
+"TLD::learn"->"TLD::generatePositiveData";
+"TLD::learn"->"FerNNClassifier::trainF";
+"TLD::learn"->"FerNNClassifier::trainNN";
+"TLD::learn"->"FerNNClassifier::show";
+"TLD::getOverlappingBoxes"->"TLD::getBBHull";
+"main"->"TLD::processFrame";
+}
diff --git a/doc/processframe.png b/doc/processframe.png
new file mode 100644
index 0000000..7c971d9
Binary files /dev/null and b/doc/processframe.png differ
diff --git a/include/FerNNClassifier.h b/include/FerNNClassifier.h
new file mode 100644
index 0000000..fb25001
--- /dev/null
+++ b/include/FerNNClassifier.h
@@ -0,0 +1,56 @@
+/*
+ * FerNNClassifier.h
+ *
+ *  Created on: Jun 14, 2011
+ *      Author: alantrrs
+ */
+
+#include <opencv2/opencv.hpp>
+#include <stdio.h>
+class FerNNClassifier{
+private:
+  float thr_fern;
+  int structSize;
+  int nstructs;
+  float valid;
+  float ncc_thesame;
+  float thr_nn;
+  int acum;
+public:
+  //Parameters
+  float thr_nn_valid;
+
+  void read(const cv::FileNode& file);
+  void prepare(const std::vector<cv::Size>& scales);
+  void getFeatures(const cv::Mat& image,const int& scale_idx,std::vector<int>& fern);
+  void update(const std::vector<int>& fern, int C, int N);
+  float measure_forest(std::vector<int> fern);
+  void trainF(const std::vector<std::pair<std::vector<int>,int> >& ferns,int resample);
+  void trainNN(const std::vector<cv::Mat>& nn_examples);
+  void NNConf(const cv::Mat& example,std::vector<int>& isin,float& rsconf,float& csconf);
+  void evaluateTh(const std::vector<std::pair<std::vector<int>,int> >& nXT,const std::vector<cv::Mat>& nExT);
+  void show();
+  //Ferns Members
+  int getNumStructs(){return nstructs;}
+  float getFernTh(){return thr_fern;}
+  float getNNTh(){return thr_nn;}
+  struct Feature
+      {
+          uchar x1, y1, x2, y2;
+          Feature() : x1(0), y1(0), x2(0), y2(0) {}
+          Feature(int _x1, int _y1, int _x2, int _y2)
+          : x1((uchar)_x1), y1((uchar)_y1), x2((uchar)_x2), y2((uchar)_y2)
+          {}
+          bool operator ()(const cv::Mat& patch) const
+          { return patch.at<uchar>(y1,x1) > patch.at<uchar>(y2, x2); }
+      };
+  std::vector<std::vector<Feature> > features; //Ferns features (one std::vector for each scale)
+  std::vector< std::vector<int> > nCounter; //negative counter
+  std::vector< std::vector<int> > pCounter; //positive counter
+  std::vector< std::vector<float> > posteriors; //Ferns posteriors
+  float thrN; //Negative threshold
+  float thrP;  //Positive thershold
+  //NN Members
+  std::vector<cv::Mat> pEx; //NN positive examples
+  std::vector<cv::Mat> nEx; //NN negative examples
+};
diff --git a/include/LKTracker.h b/include/LKTracker.h
new file mode 100644
index 0000000..d3e7704
--- /dev/null
+++ b/include/LKTracker.h
@@ -0,0 +1,26 @@
+#include<tld_utils.h>
+#include <opencv2/opencv.hpp>
+
+
+class LKTracker{
+private:
+  std::vector<cv::Point2f> pointsFB;
+  cv::Size window_size;
+  int level;
+  std::vector<uchar> status;
+  std::vector<uchar> FB_status;
+  std::vector<float> similarity;
+  std::vector<float> FB_error;
+  float simmed;
+  float fbmed;
+  cv::TermCriteria term_criteria;
+  float lambda;
+  void normCrossCorrelation(const cv::Mat& img1,const cv::Mat& img2, std::vector<cv::Point2f>& points1, std::vector<cv::Point2f>& points2);
+  bool filterPts(std::vector<cv::Point2f>& points1,std::vector<cv::Point2f>& points2);
+public:
+  LKTracker();
+  bool trackf2f(const cv::Mat& img1, const cv::Mat& img2,
+                std::vector<cv::Point2f> &points1, std::vector<cv::Point2f> &points2);
+  float getFB(){return fbmed;}
+};
+
diff --git a/include/TLD.h b/include/TLD.h
new file mode 100644
index 0000000..16a70f9
--- /dev/null
+++ b/include/TLD.h
@@ -0,0 +1,143 @@
+#include <opencv2/opencv.hpp>
+#include <opencv2/legacy/legacy.hpp>
+#include <tld_utils.h>
+#include <LKTracker.h>
+#include <FerNNClassifier.h>
+#include <fstream>
+
+
+//Bounding Boxes
+struct BoundingBox : public cv::Rect {
+  BoundingBox(){}
+  BoundingBox(cv::Rect r): cv::Rect(r){}
+public:
+  float overlap;        //Overlap with current Bounding Box
+  int sidx;             //scale index
+};
+
+//Detection structure
+struct DetStruct {
+    std::vector<int> bb;
+    std::vector<std::vector<int> > patt;
+    std::vector<float> conf1;
+    std::vector<float> conf2;
+    std::vector<std::vector<int> > isin;
+    std::vector<cv::Mat> patch;
+  };
+//Temporal structure
+  struct TempStruct {
+    std::vector<std::vector<int> > patt;
+    std::vector<float> conf;
+  };
+
+struct OComparator{
+  OComparator(const std::vector<BoundingBox>& _grid):grid(_grid){}
+  std::vector<BoundingBox> grid;
+  bool operator()(int idx1,int idx2){
+    return grid[idx1].overlap > grid[idx2].overlap;
+  }
+};
+struct CComparator{
+  CComparator(const std::vector<float>& _conf):conf(_conf){}
+  std::vector<float> conf;
+  bool operator()(int idx1,int idx2){
+    return conf[idx1]> conf[idx2];
+  }
+};
+
+
+class TLD{
+private:
+  cv::PatchGenerator generator;
+  FerNNClassifier classifier;
+  LKTracker tracker;
+  ///Parameters
+  int bbox_step;
+  int min_win;
+  int patch_size;
+  //initial parameters for positive examples
+  int num_closest_init;
+  int num_warps_init;
+  int noise_init;
+  float angle_init;
+  float shift_init;
+  float scale_init;
+  //update parameters for positive examples
+  int num_closest_update;
+  int num_warps_update;
+  int noise_update;
+  float angle_update;
+  float shift_update;
+  float scale_update;
+  //parameters for negative examples
+  float bad_overlap;
+  float bad_patches;
+  ///Variables
+//Integral Images
+  cv::Mat iisum;
+  cv::Mat iisqsum;
+  float var;
+//Training data
+  std::vector<std::pair<std::vector<int>,int> > pX; //positive ferns <features,labels=1>
+  std::vector<std::pair<std::vector<int>,int> > nX; // negative ferns <features,labels=0>
+  cv::Mat pEx;  //positive NN example
+  std::vector<cv::Mat> nEx; //negative NN examples
+//Test data
+  std::vector<std::pair<std::vector<int>,int> > nXT; //negative data to Test
+  std::vector<cv::Mat> nExT; //negative NN examples to Test
+//Last frame data
+  BoundingBox lastbox;
+  bool lastvalid;
+  float lastconf;
+//Current frame data
+  //Tracker data
+  bool tracked;
+  BoundingBox tbb;
+  bool tvalid;
+  float tconf;
+  //Detector data
+  TempStruct tmp;
+  DetStruct dt;
+  std::vector<BoundingBox> dbb;
+  std::vector<bool> dvalid;
+  std::vector<float> dconf;
+  bool detected;
+
+
+  //Bounding Boxes
+  std::vector<BoundingBox> grid;
+  std::vector<cv::Size> scales;
+  std::vector<int> good_boxes; //indexes of bboxes with overlap > 0.6
+  std::vector<int> bad_boxes; //indexes of bboxes with overlap < 0.2
+  BoundingBox bbhull; // hull of good_boxes
+  BoundingBox best_box; // maximum overlapping bbox
+
+public:
+  //Constructors
+  TLD();
+  TLD(const cv::FileNode& file);
+  void read(const cv::FileNode& file);
+  //Methods
+  void init(const cv::Mat& frame1,const cv::Rect &box, FILE* bb_file);
+  void generatePositiveData(const cv::Mat& frame, int num_warps);
+  void generateNegativeData(const cv::Mat& frame);
+  void processFrame(const cv::Mat& img1,const cv::Mat& img2,std::vector<cv::Point2f>& points1,std::vector<cv::Point2f>& points2,
+      BoundingBox& bbnext,bool& lastboxfound, bool tl,FILE* bb_file);
+  void track(const cv::Mat& img1, const cv::Mat& img2,std::vector<cv::Point2f>& points1,std::vector<cv::Point2f>& points2);
+  void detect(const cv::Mat& frame);
+  void clusterConf(const std::vector<BoundingBox>& dbb,const std::vector<float>& dconf,std::vector<BoundingBox>& cbb,std::vector<float>& cconf);
+  void evaluate();
+  void learn(const cv::Mat& img);
+  //Tools
+  void buildGrid(const cv::Mat& img, const cv::Rect& box);
+  float bbOverlap(const BoundingBox& box1,const BoundingBox& box2);
+  void getOverlappingBoxes(const cv::Rect& box1,int num_closest);
+  void getBBHull();
+  void getPattern(const cv::Mat& img, cv::Mat& pattern,cv::Scalar& mean,cv::Scalar& stdev);
+  void bbPoints(std::vector<cv::Point2f>& points, const BoundingBox& bb);
+  void bbPredict(const std::vector<cv::Point2f>& points1,const std::vector<cv::Point2f>& points2,
+      const BoundingBox& bb1,BoundingBox& bb2);
+  double getVar(const BoundingBox& box,const cv::Mat& sum,const cv::Mat& sqsum);
+  bool bbComp(const BoundingBox& bb1,const BoundingBox& bb2);
+  int clusterBB(const std::vector<BoundingBox>& dbb,std::vector<int>& indexes);
+};
diff --git a/include/tld_utils.h b/include/tld_utils.h
new file mode 100644
index 0000000..d883cd4
--- /dev/null
+++ b/include/tld_utils.h
@@ -0,0 +1,15 @@
+#include <opencv2/opencv.hpp>
+#pragma once
+
+
+
+void drawBox(cv::Mat& image, CvRect box, cv::Scalar color = cvScalarAll(255), int thick=1); 
+
+void drawPoints(cv::Mat& image, std::vector<cv::Point2f> points,cv::Scalar color=cv::Scalar::all(255));
+
+cv::Mat createMask(const cv::Mat& image, CvRect box);
+
+float median(std::vector<float> v);
+
+std::vector<int> index_shuffle(int begin,int end);
+
diff --git a/parameters.yml b/parameters.yml
new file mode 100644
index 0000000..d77bbee
--- /dev/null
+++ b/parameters.yml
@@ -0,0 +1,30 @@
+%YAML:1.0
+Parameters:
+   min_win: 15
+   patch_size: 15
+   ncc_thesame: 0.95
+   valid: 0.5
+   num_trees: 10
+   num_features: 13
+   thr_fern: 0.6
+   thr_nn: 0.65
+   thr_nn_valid: 0.7
+   num_closest_init: 10
+   num_warps_init: 20
+   noise_init: 5
+   angle_init: 20
+   shift_init: 0.02
+   scale_init: 0.02
+   num_closest_update: 10
+   num_warps_update: 10
+   noise_update: 5
+   angle_update: 10
+   shift_update: 0.02
+   scale_update: 0.02
+   overlap: 0.2
+   num_patches: 100
+   bb_x: 288
+   bb_y: 36
+   bb_w: 25
+   bb_h: 42   
+
diff --git a/src/CMakeLists.txt b/src/CMakeLists.txt
new file mode 100644
index 0000000..1ace99e
--- /dev/null
+++ b/src/CMakeLists.txt
@@ -0,0 +1,30 @@
+#Set minimum version requered
+cmake_minimum_required(VERSION 2.4.6)
+#just to avoid the warning
+if(COMMAND cmake_policy)
+     cmake_policy(SET CMP0003 NEW)
+endif(COMMAND cmake_policy)
+#set project name
+project(TLD)
+#Append path to the module path
+list(APPEND CMAKE_MODULE_PATH ${PROJECT_SOURCE_DIR})
+#OpenCV
+#set(OpenCV_DIR /home/install/opencv2.4.12.1)
+find_package(OpenCV REQUIRED)
+#set the default path for built executables to the "bin" directory
+set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/../bin)
+#set the default path for built libraries to the "lib" directory
+set(LIBRARY_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/../lib)
+#set the include directories
+include_directories (${PROJECT_SOURCE_DIR}/../include	${OpenCV_INCLUDE_DIRS})
+#libraries
+add_library(tld_utils tld_utils.cpp)
+add_library(LKTracker LKTracker.cpp)
+add_library(ferNN FerNNClassifier.cpp)
+add_library(tld TLD.cpp)
+#executables
+add_executable(run_tld run_tld.cpp)
+#link the libraries
+target_link_libraries(run_tld tld LKTracker ferNN tld_utils ${OpenCV_LIBS})
+#set optimization level
+set(CMAKE_BUILD_TYPE Release)
diff --git a/src/FerNNClassifier.cpp b/src/FerNNClassifier.cpp
new file mode 100644
index 0000000..639c65c
--- /dev/null
+++ b/src/FerNNClassifier.cpp
@@ -0,0 +1,224 @@
+/*
+ * FerNNClassifier.cpp
+ *
+ *  Created on: Jun 14, 2011
+ *      Author: alantrrs
+ */
+
+#include <FerNNClassifier.h>
+
+using namespace cv;
+using namespace std;
+
+void FerNNClassifier::read(const FileNode& file){
+  ///Classifier Parameters
+  valid = (float)file["valid"];
+  ncc_thesame = (float)file["ncc_thesame"];
+  nstructs = (int)file["num_trees"];
+  structSize = (int)file["num_features"];
+  thr_fern = (float)file["thr_fern"];
+  thr_nn = (float)file["thr_nn"];
+  thr_nn_valid = (float)file["thr_nn_valid"];
+}
+
+void FerNNClassifier::prepare(const vector<Size>& scales){
+  acum = 0;
+  //Initialize test locations for features
+  int totalFeatures = nstructs*structSize;
+  features = vector<vector<Feature> >(scales.size(),vector<Feature> (totalFeatures));
+  RNG& rng = theRNG();
+  float x1f,x2f,y1f,y2f;
+  int x1, x2, y1, y2;
+  for (int i=0;i<totalFeatures;i++){
+      x1f = (float)rng;
+      y1f = (float)rng;
+      x2f = (float)rng;
+      y2f = (float)rng;
+      for (int s=0;s<scales.size();s++){
+          x1 = x1f * scales[s].width;
+          y1 = y1f * scales[s].height;
+          x2 = x2f * scales[s].width;
+          y2 = y2f * scales[s].height;
+          features[s][i] = Feature(x1, y1, x2, y2);
+      }
+
+  }
+  //Thresholds
+  thrN = 0.5*nstructs;
+
+  //Initialize Posteriors
+  for (int i = 0; i<nstructs; i++) {
+      posteriors.push_back(vector<float>(pow(2.0,structSize), 0));
+      pCounter.push_back(vector<int>(pow(2.0,structSize), 0));
+      nCounter.push_back(vector<int>(pow(2.0,structSize), 0));
+  }
+}
+
+void FerNNClassifier::getFeatures(const cv::Mat& image,const int& scale_idx, vector<int>& fern){
+  int leaf;
+  for (int t=0;t<nstructs;t++){
+      leaf=0;
+      for (int f=0; f<structSize; f++){
+          leaf = (leaf << 1) + features[scale_idx][t*nstructs+f](image);
+      }
+      fern[t]=leaf;
+  }
+}
+
+float FerNNClassifier::measure_forest(vector<int> fern) {
+  float votes = 0;
+  for (int i = 0; i < nstructs; i++) {
+      votes += posteriors[i][fern[i]];
+  }
+  return votes;
+}
+
+void FerNNClassifier::update(const vector<int>& fern, int C, int N) {
+  int idx;
+  for (int i = 0; i < nstructs; i++) {
+      idx = fern[i];
+      (C==1) ? pCounter[i][idx] += N : nCounter[i][idx] += N;
+      if (pCounter[i][idx]==0) {
+          posteriors[i][idx] = 0;
+      } else {
+          posteriors[i][idx] = ((float)(pCounter[i][idx]))/(pCounter[i][idx] + nCounter[i][idx]);
+      }
+  }
+}
+
+void FerNNClassifier::trainF(const vector<std::pair<vector<int>,int> >& ferns,int resample){
+  // Conf = function(2,X,Y,Margin,Bootstrap,Idx)
+  //                 0 1 2 3      4         5
+  //  double *X     = mxGetPr(prhs[1]); -> ferns[i].first
+  //  int numX      = mxGetN(prhs[1]);  -> ferns.size()
+  //  double *Y     = mxGetPr(prhs[2]); ->ferns[i].second
+  //  double thrP   = *mxGetPr(prhs[3]) * nTREES; ->threshold*nstructs
+  //  int bootstrap = (int) *mxGetPr(prhs[4]); ->resample
+  thrP = thr_fern*nstructs;                                                          // int step = numX / 10;
+  //for (int j = 0; j < resample; j++) {                      // for (int j = 0; j < bootstrap; j++) {
+      for (int i = 0; i < ferns.size(); i++){               //   for (int i = 0; i < step; i++) {
+                                                            //     for (int k = 0; k < 10; k++) {
+                                                            //       int I = k*step + i;//box index
+                                                            //       double *x = X+nTREES*I; //tree index
+          if(ferns[i].second==1){                           //       if (Y[I] == 1) {
+              if(measure_forest(ferns[i].first)<=thrP)      //         if (measure_forest(x) <= thrP)
+                update(ferns[i].first,1,1);                 //             update(x,1,1);
+          }else{                                            //        }else{
+              if (measure_forest(ferns[i].first) >= thrN)   //         if (measure_forest(x) >= thrN)
+                update(ferns[i].first,0,1);                 //             update(x,0,1);
+          }
+      }
+  //}
+}
+
+void FerNNClassifier::trainNN(const vector<cv::Mat>& nn_examples){
+  float conf,dummy;
+  vector<int> y(nn_examples.size(),0);
+  y[0]=1;
+  vector<int> isin;
+  for (int i=0;i<nn_examples.size();i++){                          //  For each example
+      NNConf(nn_examples[i],isin,conf,dummy);                      //  Measure Relative similarity
+      if (y[i]==1 && conf<=thr_nn){                                //    if y(i) == 1 && conf1 <= tld.model.thr_nn % 0.65
+          if (isin[1]<0){                                          //      if isnan(isin(2))
+              pEx = vector<Mat>(1,nn_examples[i]);                 //        tld.pex = x(:,i);
+              continue;                                            //        continue;
+          }                                                        //      end
+          //pEx.insert(pEx.begin()+isin[1],nn_examples[i]);        //      tld.pex = [tld.pex(:,1:isin(2)) x(:,i) tld.pex(:,isin(2)+1:end)]; % add to model
+          pEx.push_back(nn_examples[i]);
+      }                                                            //    end
+      if(y[i]==0 && conf>0.5)                                      //  if y(i) == 0 && conf1 > 0.5
+        nEx.push_back(nn_examples[i]);                             //    tld.nex = [tld.nex x(:,i)];
+
+  }                                                                 //  end
+  acum++;
+  printf("%d. Trained NN examples: %d positive %d negative\n",acum,(int)pEx.size(),(int)nEx.size());
+}                                                                  //  end
+
+
+void FerNNClassifier::NNConf(const Mat& example, vector<int>& isin,float& rsconf,float& csconf){
+  /*Inputs:
+   * -NN Patch
+   * Outputs:
+   * -Relative Similarity (rsconf), Conservative Similarity (csconf), In pos. set|Id pos set|In neg. set (isin)
+   */
+  isin=vector<int>(3,-1);
+  if (pEx.empty()){ //if isempty(tld.pex) % IF positive examples in the model are not defined THEN everything is negative
+      rsconf = 0; //    conf1 = zeros(1,size(x,2));
+      csconf=0;
+      return;
+  }
+  if (nEx.empty()){ //if isempty(tld.nex) % IF negative examples in the model are not defined THEN everything is positive
+      rsconf = 1;   //    conf1 = ones(1,size(x,2));
+      csconf=1;
+      return;
+  }
+  Mat ncc(1,1,CV_32F);
+  float nccP,csmaxP,maxP=0;
+  bool anyP=false;
+  int maxPidx,validatedPart = ceil(pEx.size()*valid);
+  float nccN, maxN=0;
+  bool anyN=false;
+  for (int i=0;i<pEx.size();i++){
+      matchTemplate(pEx[i],example,ncc,CV_TM_CCORR_NORMED);      // measure NCC to positive examples
+      nccP=(((float*)ncc.data)[0]+1)*0.5;
+      if (nccP>ncc_thesame)
+        anyP=true;
+      if(nccP > maxP){
+          maxP=nccP;
+          maxPidx = i;
+          if(i<validatedPart)
+            csmaxP=maxP;
+      }
+  }
+  for (int i=0;i<nEx.size();i++){
+      matchTemplate(nEx[i],example,ncc,CV_TM_CCORR_NORMED);     //measure NCC to negative examples
+      nccN=(((float*)ncc.data)[0]+1)*0.5;
+      if (nccN>ncc_thesame)
+        anyN=true;
+      if(nccN > maxN)
+        maxN=nccN;
+  }
+  //set isin
+  if (anyP) isin[0]=1;  //if he query patch is highly correlated with any positive patch in the model then it is considered to be one of them
+  isin[1]=maxPidx;      //get the index of the maximall correlated positive patch
+  if (anyN) isin[2]=1;  //if  the query patch is highly correlated with any negative patch in the model then it is considered to be one of them
+  //Measure Relative Similarity
+  float dN=1-maxN;
+  float dP=1-maxP;
+  rsconf = (float)dN/(dN+dP);
+  //Measure Conservative Similarity
+  dP = 1 - csmaxP;
+  csconf =(float)dN / (dN + dP);
+}
+
+void FerNNClassifier::evaluateTh(const vector<pair<vector<int>,int> >& nXT,const vector<cv::Mat>& nExT){
+float fconf;
+  for (int i=0;i<nXT.size();i++){
+    fconf = (float) measure_forest(nXT[i].first)/nstructs;
+    if (fconf>thr_fern)
+      thr_fern=fconf;
+}
+  vector <int> isin;
+  float conf,dummy;
+  for (int i=0;i<nExT.size();i++){
+      NNConf(nExT[i],isin,conf,dummy);
+      if (conf>thr_nn)
+        thr_nn=conf;
+  }
+  if (thr_nn>thr_nn_valid)
+    thr_nn_valid = thr_nn;
+}
+
+void FerNNClassifier::show(){
+  Mat examples((int)pEx.size()*pEx[0].rows,pEx[0].cols,CV_8U);
+  double minval;
+  Mat ex(pEx[0].rows,pEx[0].cols,pEx[0].type());
+  for (int i=0;i<pEx.size();i++){
+    minMaxLoc(pEx[i],&minval);
+    pEx[i].copyTo(ex);
+    ex = ex-minval;
+    Mat tmp = examples.rowRange(Range(i*pEx[i].rows,(i+1)*pEx[i].rows));
+    ex.convertTo(tmp,CV_8U);
+  }
+  imshow("Examples",examples);
+}
diff --git a/src/LKTracker.cpp b/src/LKTracker.cpp
new file mode 100644
index 0000000..df3c8ae
--- /dev/null
+++ b/src/LKTracker.cpp
@@ -0,0 +1,107 @@
+#include <LKTracker.h>
+using namespace cv;
+
+LKTracker::LKTracker(){
+  term_criteria = TermCriteria( TermCriteria::COUNT+TermCriteria::EPS, 20, 0.03);
+  window_size = Size(4,4);
+  level = 5;
+  lambda = 0.5;
+}
+
+
+bool LKTracker::trackf2f(const Mat& img1, const Mat& img2,vector<Point2f> &points1, vector<cv::Point2f> &points2){
+  //TODO!:implement c function cvCalcOpticalFlowPyrLK() or Faster tracking function
+  //Forward-Backward tracking
+  calcOpticalFlowPyrLK( img1,img2, points1, points2, status,similarity, window_size, level, term_criteria, lambda, 0);
+  calcOpticalFlowPyrLK( img2,img1, points2, pointsFB, FB_status,FB_error, window_size, level, term_criteria, lambda, 0);
+  //Compute the real FB-error
+  for( int i= 0; i<points1.size(); ++i ){
+        FB_error[i] = norm(pointsFB[i]-points1[i]);
+  }
+  //Filter out points with FB_error[i] > median(FB_error) && points with sim_error[i] > median(sim_error)
+  normCrossCorrelation(img1,img2,points1,points2);
+  return filterPts(points1,points2);
+}
+
+void LKTracker::normCrossCorrelation(const Mat& img1,const Mat& img2, vector<Point2f>& points1, vector<Point2f>& points2) {
+        Mat rec0(10,10,CV_8U);
+        Mat rec1(10,10,CV_8U);
+        Mat res(1,1,CV_32F);
+
+        for (int i = 0; i < points1.size(); i++) {
+                if (status[i] == 1) {
+                        getRectSubPix( img1, Size(10,10), points1[i],rec0 );
+                        getRectSubPix( img2, Size(10,10), points2[i],rec1);
+                        matchTemplate( rec0,rec1, res, CV_TM_CCOEFF_NORMED);
+                        similarity[i] = ((float *)(res.data))[0];
+
+                } else {
+                        similarity[i] = 0.0;
+                }
+        }
+        rec0.release();
+        rec1.release();
+        res.release();
+}
+
+
+bool LKTracker::filterPts(vector<Point2f>& points1,vector<Point2f>& points2){
+  //Get Error Medians
+  simmed = median(similarity);
+  size_t i, k;
+  for( i=k = 0; i<points2.size(); ++i ){
+        if( !status[i])
+          continue;
+        if(similarity[i]> simmed){
+          points1[k] = points1[i];
+          points2[k] = points2[i];
+          FB_error[k] = FB_error[i];
+          k++;
+        }
+    }
+  if (k==0)
+    return false;
+  points1.resize(k);
+  points2.resize(k);
+  FB_error.resize(k);
+
+  fbmed = median(FB_error);
+  for( i=k = 0; i<points2.size(); ++i ){
+      if( !status[i])
+        continue;
+      if(FB_error[i] <= fbmed){
+        points1[k] = points1[i];
+        points2[k] = points2[i];
+        k++;
+      }
+  }
+  points1.resize(k);
+  points2.resize(k);
+  if (k>0)
+    return true;
+  else
+    return false;
+}
+
+
+
+
+/*
+ * old OpenCV style
+void LKTracker::init(Mat img0, vector<Point2f> &points){
+  //Preallocate
+  //pyr1 = cvCreateImage(Size(img1.width+8,img1.height/3),IPL_DEPTH_32F,1);
+  //pyr2 = cvCreateImage(Size(img1.width+8,img1.height/3),IPL_DEPTH_32F,1);
+  //const int NUM_PTS = points.size();
+  //status = new char[NUM_PTS];
+  //track_error = new float[NUM_PTS];
+  //FB_error = new float[NUM_PTS];
+}
+
+
+void LKTracker::trackf2f(..){
+  cvCalcOpticalFlowPyrLK( &img1, &img2, pyr1, pyr1, points1, points2, points1.size(), window_size, level, status, track_error, term_criteria, CV_LKFLOW_INITIAL_GUESSES);
+  cvCalcOpticalFlowPyrLK( &img2, &img1, pyr2, pyr1, points2, pointsFB, points2.size(),window_size, level, 0, 0, term_criteria, CV_LKFLOW_INITIAL_GUESSES | CV_LKFLOW_PYR_A_READY | CV_LKFLOW_PYR_B_READY );
+}
+*/
+
diff --git a/src/TLD.cpp b/src/TLD.cpp
new file mode 100644
index 0000000..e3d3ebc
--- /dev/null
+++ b/src/TLD.cpp
@@ -0,0 +1,776 @@
+/*
+ * TLD.cpp
+ *
+ *  Created on: Jun 9, 2011
+ *      Author: alantrrs
+ */
+
+#include <TLD.h>
+#include <stdio.h>
+using namespace cv;
+using namespace std;
+
+
+TLD::TLD()
+{
+}
+TLD::TLD(const FileNode& file){
+  read(file);
+}
+
+void TLD::read(const FileNode& file){
+  ///Bounding Box Parameters
+  min_win = (int)file["min_win"];
+  ///Genarator Parameters
+  //initial parameters for positive examples
+  patch_size = (int)file["patch_size"];
+  num_closest_init = (int)file["num_closest_init"];
+  num_warps_init = (int)file["num_warps_init"];
+  noise_init = (int)file["noise_init"];
+  angle_init = (float)file["angle_init"];
+  shift_init = (float)file["shift_init"];
+  scale_init = (float)file["scale_init"];
+  //update parameters for positive examples
+  num_closest_update = (int)file["num_closest_update"];
+  num_warps_update = (int)file["num_warps_update"];
+  noise_update = (int)file["noise_update"];
+  angle_update = (float)file["angle_update"];
+  shift_update = (float)file["shift_update"];
+  scale_update = (float)file["scale_update"];
+  //parameters for negative examples
+  bad_overlap = (float)file["overlap"];
+  bad_patches = (int)file["num_patches"];
+  classifier.read(file);
+}
+
+void TLD::init(const Mat& frame1,const Rect& box,FILE* bb_file){
+  //bb_file = fopen("bounding_boxes.txt","w");
+  //Get Bounding Boxes
+    buildGrid(frame1,box);
+    printf("Created %d bounding boxes\n",(int)grid.size());
+  ///Preparation
+  //allocation
+  iisum.create(frame1.rows+1,frame1.cols+1,CV_32F);
+  iisqsum.create(frame1.rows+1,frame1.cols+1,CV_64F);
+  dconf.reserve(100);
+  dbb.reserve(100);
+  bbox_step =7;
+  //tmp.conf.reserve(grid.size());
+  tmp.conf = vector<float>(grid.size());
+  tmp.patt = vector<vector<int> >(grid.size(),vector<int>(10,0));
+  //tmp.patt.reserve(grid.size());
+  dt.bb.reserve(grid.size());
+  good_boxes.reserve(grid.size());
+  bad_boxes.reserve(grid.size());
+  pEx.create(patch_size,patch_size,CV_64F);
+  //Init Generator
+  generator = PatchGenerator (0,0,noise_init,true,1-scale_init,1+scale_init,-angle_init*CV_PI/180,angle_init*CV_PI/180,-angle_init*CV_PI/180,angle_init*CV_PI/180);
+  getOverlappingBoxes(box,num_closest_init);
+  printf("Found %d good boxes, %d bad boxes\n",(int)good_boxes.size(),(int)bad_boxes.size());
+  printf("Best Box: %d %d %d %d\n",best_box.x,best_box.y,best_box.width,best_box.height);
+  printf("Bounding box hull: %d %d %d %d\n",bbhull.x,bbhull.y,bbhull.width,bbhull.height);
+  //Correct Bounding Box
+  lastbox=best_box;
+  lastconf=1;
+  lastvalid=true;
+  //Print
+  fprintf(bb_file,"%d,%d,%d,%d,%f\n",lastbox.x,lastbox.y,lastbox.br().x,lastbox.br().y,lastconf);
+  //Prepare Classifier
+  classifier.prepare(scales);
+  ///Generate Data
+  // Generate positive data
+  generatePositiveData(frame1,num_warps_init);
+  // Set variance threshold
+  Scalar stdev, mean;
+  meanStdDev(frame1(best_box),mean,stdev);
+  integral(frame1,iisum,iisqsum);
+  var = pow(stdev.val[0],2)*0.5; //getVar(best_box,iisum,iisqsum);
+  cout << "variance: " << var << endl;
+  //check variance
+  double vr =  getVar(best_box,iisum,iisqsum)*0.5;
+  cout << "check variance: " << vr << endl;
+  // Generate negative data
+  generateNegativeData(frame1);
+  //Split Negative Ferns into Training and Testing sets (they are already shuffled)
+  int half = (int)nX.size()*0.5f;
+  nXT.assign(nX.begin()+half,nX.end());
+  nX.resize(half);
+  ///Split Negative NN Examples into Training and Testing sets
+  half = (int)nEx.size()*0.5f;
+  nExT.assign(nEx.begin()+half,nEx.end());
+  nEx.resize(half);
+  //Merge Negative Data with Positive Data and shuffle it
+  vector<pair<vector<int>,int> > ferns_data(nX.size()+pX.size());
+  vector<int> idx = index_shuffle(0,ferns_data.size());
+  int a=0;
+  for (int i=0;i<pX.size();i++){
+      ferns_data[idx[a]] = pX[i];
+      a++;
+  }
+  for (int i=0;i<nX.size();i++){
+      ferns_data[idx[a]] = nX[i];
+      a++;
+  }
+  //Data already have been shuffled, just putting it in the same vector
+  vector<cv::Mat> nn_data(nEx.size()+1);
+  nn_data[0] = pEx;
+  for (int i=0;i<nEx.size();i++){
+      nn_data[i+1]= nEx[i];
+  }
+  ///Training
+  classifier.trainF(ferns_data,2); //bootstrap = 2
+  classifier.trainNN(nn_data);
+  ///Threshold Evaluation on testing sets
+  classifier.evaluateTh(nXT,nExT);
+}
+
+/* Generate Positive data
+ * Inputs:
+ * - good_boxes (bbP)
+ * - best_box (bbP0)
+ * - frame (im0)
+ * Outputs:
+ * - Positive fern features (pX)
+ * - Positive NN examples (pEx)
+ */
+void TLD::generatePositiveData(const Mat& frame, int num_warps){
+  Scalar mean;
+  Scalar stdev;
+  getPattern(frame(best_box),pEx,mean,stdev);
+  //Get Fern features on warped patches
+  Mat img;
+  Mat warped;
+  GaussianBlur(frame,img,Size(9,9),1.5);
+  warped = img(bbhull);
+  RNG& rng = theRNG();
+  Point2f pt(bbhull.x+(bbhull.width-1)*0.5f,bbhull.y+(bbhull.height-1)*0.5f);
+  vector<int> fern(classifier.getNumStructs());
+  pX.clear();
+  Mat patch;
+  if (pX.capacity()<num_warps*good_boxes.size())
+    pX.reserve(num_warps*good_boxes.size());
+  int idx;
+  for (int i=0;i<num_warps;i++){
+     if (i>0)
+       generator(frame,pt,warped,bbhull.size(),rng);
+       for (int b=0;b<good_boxes.size();b++){
+         idx=good_boxes[b];
+		 patch = img(grid[idx]);
+         classifier.getFeatures(patch,grid[idx].sidx,fern);
+         pX.push_back(make_pair(fern,1));
+     }
+  }
+  printf("Positive examples generated: ferns:%d NN:1\n",(int)pX.size());
+}
+
+void TLD::getPattern(const Mat& img, Mat& pattern,Scalar& mean,Scalar& stdev){
+  //Output: resized Zero-Mean patch
+  resize(img,pattern,Size(patch_size,patch_size));
+  meanStdDev(pattern,mean,stdev);
+  pattern.convertTo(pattern,CV_32F);
+  pattern = pattern-mean.val[0];
+}
+
+void TLD::generateNegativeData(const Mat& frame){
+/* Inputs:
+ * - Image
+ * - bad_boxes (Boxes far from the bounding box)
+ * - variance (pEx variance)
+ * Outputs
+ * - Negative fern features (nX)
+ * - Negative NN examples (nEx)
+ */
+  random_shuffle(bad_boxes.begin(),bad_boxes.end());//Random shuffle bad_boxes indexes
+  int idx;
+  //Get Fern Features of the boxes with big variance (calculated using integral images)
+  int a=0;
+  //int num = std::min((int)bad_boxes.size(),(int)bad_patches*100); //limits the size of bad_boxes to try
+  printf("negative data generation started.\n");
+  vector<int> fern(classifier.getNumStructs());
+  nX.reserve(bad_boxes.size());
+  Mat patch;
+  for (int j=0;j<bad_boxes.size();j++){
+      idx = bad_boxes[j];
+          if (getVar(grid[idx],iisum,iisqsum)<var*0.5f)
+            continue;
+      patch =  frame(grid[idx]);
+	  classifier.getFeatures(patch,grid[idx].sidx,fern);
+      nX.push_back(make_pair(fern,0));
+      a++;
+  }
+  printf("Negative examples generated: ferns: %d ",a);
+  //random_shuffle(bad_boxes.begin(),bad_boxes.begin()+bad_patches);//Randomly selects 'bad_patches' and get the patterns for NN;
+  Scalar dum1, dum2;
+  nEx=vector<Mat>(bad_patches);
+  for (int i=0;i<bad_patches;i++){
+      idx=bad_boxes[i];
+	  patch = frame(grid[idx]);
+      getPattern(patch,nEx[i],dum1,dum2);
+  }
+  printf("NN: %d\n",(int)nEx.size());
+}
+
+double TLD::getVar(const BoundingBox& box,const Mat& sum,const Mat& sqsum){
+  double brs = sum.at<int>(box.y+box.height,box.x+box.width);
+  double bls = sum.at<int>(box.y+box.height,box.x);
+  double trs = sum.at<int>(box.y,box.x+box.width);
+  double tls = sum.at<int>(box.y,box.x);
+  double brsq = sqsum.at<double>(box.y+box.height,box.x+box.width);
+  double blsq = sqsum.at<double>(box.y+box.height,box.x);
+  double trsq = sqsum.at<double>(box.y,box.x+box.width);
+  double tlsq = sqsum.at<double>(box.y,box.x);
+  double mean = (brs+tls-trs-bls)/((double)box.area());
+  double sqmean = (brsq+tlsq-trsq-blsq)/((double)box.area());
+  return sqmean-mean*mean;
+}
+
+void TLD::processFrame(const cv::Mat& img1,const cv::Mat& img2,vector<Point2f>& points1,vector<Point2f>& points2,BoundingBox& bbnext,bool& lastboxfound, bool tl, FILE* bb_file){
+  vector<BoundingBox> cbb;
+  vector<float> cconf;
+  int confident_detections=0;
+  int didx; //detection index
+  ///Track
+  if(lastboxfound && tl){
+      track(img1,img2,points1,points2);
+  }
+  else{
+      tracked = false;
+  }
+  ///Detect
+  detect(img2);
+  ///Integration
+  if (tracked){
+      bbnext=tbb;
+      lastconf=tconf;
+      lastvalid=tvalid;
+      printf("Tracked\n");
+      if(detected){                                               //   if Detected
+          clusterConf(dbb,dconf,cbb,cconf);                       //   cluster detections
+          printf("Found %d clusters\n",(int)cbb.size());
+          for (int i=0;i<cbb.size();i++){
+              if (bbOverlap(tbb,cbb[i])<0.5 && cconf[i]>tconf){  //  Get index of a clusters that is far from tracker and are more confident than the tracker
+                  confident_detections++;
+                  didx=i; //detection index
+              }
+          }
+          if (confident_detections==1){                                //if there is ONE such a cluster, re-initialize the tracker
+              printf("Found a better match..reinitializing tracking\n");
+              bbnext=cbb[didx];
+              lastconf=cconf[didx];
+              lastvalid=false;
+          }
+          else {
+              printf("%d confident cluster was found\n",confident_detections);
+              int cx=0,cy=0,cw=0,ch=0;
+              int close_detections=0;
+              for (int i=0;i<dbb.size();i++){
+                  if(bbOverlap(tbb,dbb[i])>0.7){                     // Get mean of close detections
+                      cx += dbb[i].x;
+                      cy +=dbb[i].y;
+                      cw += dbb[i].width;
+                      ch += dbb[i].height;
+                      close_detections++;
+                      printf("weighted detection: %d %d %d %d\n",dbb[i].x,dbb[i].y,dbb[i].width,dbb[i].height);
+                  }
+              }
+              if (close_detections>0){
+                  bbnext.x = cvRound((float)(10*tbb.x+cx)/(float)(10+close_detections));   // weighted average trackers trajectory with the close detections
+                  bbnext.y = cvRound((float)(10*tbb.y+cy)/(float)(10+close_detections));
+                  bbnext.width = cvRound((float)(10*tbb.width+cw)/(float)(10+close_detections));
+                  bbnext.height =  cvRound((float)(10*tbb.height+ch)/(float)(10+close_detections));
+                  printf("Tracker bb: %d %d %d %d\n",tbb.x,tbb.y,tbb.width,tbb.height);
+                  printf("Average bb: %d %d %d %d\n",bbnext.x,bbnext.y,bbnext.width,bbnext.height);
+                  printf("Weighting %d close detection(s) with tracker..\n",close_detections);
+              }
+              else{
+                printf("%d close detections were found\n",close_detections);
+
+              }
+          }
+      }
+  }
+  else{                                       //   If NOT tracking
+      printf("Not tracking..\n");
+      lastboxfound = false;
+      lastvalid = false;
+      if(detected){                           //  and detector is defined
+          clusterConf(dbb,dconf,cbb,cconf);   //  cluster detections
+          printf("Found %d clusters\n",(int)cbb.size());
+          if (cconf.size()==1){
+              bbnext=cbb[0];
+              lastconf=cconf[0];
+              printf("Confident detection..reinitializing tracker\n");
+              lastboxfound = true;
+          }
+      }
+  }
+  lastbox=bbnext;
+  if (lastboxfound)
+    fprintf(bb_file,"%d,%d,%d,%d,%f\n",lastbox.x,lastbox.y,lastbox.br().x,lastbox.br().y,lastconf);
+  else
+    fprintf(bb_file,"NaN,NaN,NaN,NaN,NaN\n");
+  if (lastvalid && tl)
+    learn(img2);
+}
+
+
+void TLD::track(const Mat& img1, const Mat& img2,vector<Point2f>& points1,vector<Point2f>& points2){
+  /*Inputs:
+   * -current frame(img2), last frame(img1), last Bbox(bbox_f[0]).
+   *Outputs:
+   *- Confidence(tconf), Predicted bounding box(tbb),Validity(tvalid), points2 (for display purposes only)
+   */
+  //Generate points
+  bbPoints(points1,lastbox);
+  if (points1.size()<1){
+      printf("BB= %d %d %d %d, Points not generated\n",lastbox.x,lastbox.y,lastbox.width,lastbox.height);
+      tvalid=false;
+      tracked=false;
+      return;
+  }
+  vector<Point2f> points = points1;
+  //Frame-to-frame tracking with forward-backward error cheking
+  tracked = tracker.trackf2f(img1,img2,points,points2);
+  if (tracked){
+      //Bounding box prediction
+      bbPredict(points,points2,lastbox,tbb);
+      if (tracker.getFB()>10 || tbb.x>img2.cols ||  tbb.y>img2.rows || tbb.br().x < 1 || tbb.br().y <1){
+          tvalid =false; //too unstable prediction or bounding box out of image
+          tracked = false;
+          printf("Too unstable predictions FB error=%f\n",tracker.getFB());
+          return;
+      }
+      //Estimate Confidence and Validity
+      Mat pattern;
+      Scalar mean, stdev;
+      BoundingBox bb;
+      bb.x = max(tbb.x,0);
+      bb.y = max(tbb.y,0);
+      bb.width = min(min(img2.cols-tbb.x,tbb.width),min(tbb.width,tbb.br().x));
+      bb.height = min(min(img2.rows-tbb.y,tbb.height),min(tbb.height,tbb.br().y));
+      getPattern(img2(bb),pattern,mean,stdev);
+      vector<int> isin;
+      float dummy;
+      classifier.NNConf(pattern,isin,dummy,tconf); //Conservative Similarity
+      tvalid = lastvalid;
+      if (tconf>classifier.thr_nn_valid){
+          tvalid =true;
+      }
+  }
+  else
+    printf("No points tracked\n");
+
+}
+
+void TLD::bbPoints(vector<cv::Point2f>& points,const BoundingBox& bb){
+  int max_pts=10;
+  int margin_h=0;
+  int margin_v=0;
+  int stepx = ceil((bb.width-2*margin_h)/max_pts);
+  int stepy = ceil((bb.height-2*margin_v)/max_pts);
+  for (int y=bb.y+margin_v;y<bb.y+bb.height-margin_v;y+=stepy){
+      for (int x=bb.x+margin_h;x<bb.x+bb.width-margin_h;x+=stepx){
+          points.push_back(Point2f(x,y));
+      }
+  }
+}
+
+void TLD::bbPredict(const vector<cv::Point2f>& points1,const vector<cv::Point2f>& points2,
+                    const BoundingBox& bb1,BoundingBox& bb2)    {
+  int npoints = (int)points1.size();
+  vector<float> xoff(npoints);
+  vector<float> yoff(npoints);
+  printf("tracked points : %d\n",npoints);
+  for (int i=0;i<npoints;i++){
+      xoff[i]=points2[i].x-points1[i].x;
+      yoff[i]=points2[i].y-points1[i].y;
+  }
+  float dx = median(xoff);
+  float dy = median(yoff);
+  float s;
+  if (npoints>1){
+      vector<float> d;
+      d.reserve(npoints*(npoints-1)/2);
+      for (int i=0;i<npoints;i++){
+          for (int j=i+1;j<npoints;j++){
+              d.push_back(norm(points2[i]-points2[j])/norm(points1[i]-points1[j]));
+          }
+      }
+      s = median(d);
+  }
+  else {
+      s = 1.0;
+  }
+  float s1 = 0.5*(s-1)*bb1.width;
+  float s2 = 0.5*(s-1)*bb1.height;
+  printf("s= %f s1= %f s2= %f \n",s,s1,s2);
+  bb2.x = round( bb1.x + dx -s1);
+  bb2.y = round( bb1.y + dy -s2);
+  bb2.width = round(bb1.width*s);
+  bb2.height = round(bb1.height*s);
+  printf("predicted bb: %d %d %d %d\n",bb2.x,bb2.y,bb2.br().x,bb2.br().y);
+}
+
+void TLD::detect(const cv::Mat& frame){
+  //cleaning
+  dbb.clear();
+  dconf.clear();
+  dt.bb.clear();
+  double t = (double)getTickCount();
+  Mat img(frame.rows,frame.cols,CV_8U);
+  integral(frame,iisum,iisqsum);
+  GaussianBlur(frame,img,Size(9,9),1.5);
+  int numtrees = classifier.getNumStructs();
+  float fern_th = classifier.getFernTh();
+  vector <int> ferns(10);
+  float conf;
+  int a=0;
+  Mat patch;
+  for (int i=0;i<grid.size();i++){//FIXME: BottleNeck
+      if (getVar(grid[i],iisum,iisqsum)>=var){
+          a++;
+		  patch = img(grid[i]);
+          classifier.getFeatures(patch,grid[i].sidx,ferns);
+          conf = classifier.measure_forest(ferns);
+          tmp.conf[i]=conf;
+          tmp.patt[i]=ferns;
+          if (conf>numtrees*fern_th){
+              dt.bb.push_back(i);
+          }
+      }
+      else
+        tmp.conf[i]=0.0;
+  }
+  int detections = dt.bb.size();
+  printf("%d Bounding boxes passed the variance filter\n",a);
+  printf("%d Initial detection from Fern Classifier\n",detections);
+  if (detections>100){
+      nth_element(dt.bb.begin(),dt.bb.begin()+100,dt.bb.end(),CComparator(tmp.conf));
+      dt.bb.resize(100);
+      detections=100;
+  }
+//  for (int i=0;i<detections;i++){
+//        drawBox(img,grid[dt.bb[i]]);
+//    }
+//  imshow("detections",img);
+  if (detections==0){
+        detected=false;
+        return;
+      }
+  printf("Fern detector made %d detections ",detections);
+  t=(double)getTickCount()-t;
+  printf("in %gms\n", t*1000/getTickFrequency());
+                                                                       //  Initialize detection structure
+  dt.patt = vector<vector<int> >(detections,vector<int>(10,0));        //  Corresponding codes of the Ensemble Classifier
+  dt.conf1 = vector<float>(detections);                                //  Relative Similarity (for final nearest neighbour classifier)
+  dt.conf2 =vector<float>(detections);                                 //  Conservative Similarity (for integration with tracker)
+  dt.isin = vector<vector<int> >(detections,vector<int>(3,-1));        //  Detected (isin=1) or rejected (isin=0) by nearest neighbour classifier
+  dt.patch = vector<Mat>(detections,Mat(patch_size,patch_size,CV_32F));//  Corresponding patches
+  int idx;
+  Scalar mean, stdev;
+  float nn_th = classifier.getNNTh();
+  for (int i=0;i<detections;i++){                                         //  for every remaining detection
+      idx=dt.bb[i];                                                       //  Get the detected bounding box index
+	  patch = frame(grid[idx]);
+      getPattern(patch,dt.patch[i],mean,stdev);                //  Get pattern within bounding box
+      classifier.NNConf(dt.patch[i],dt.isin[i],dt.conf1[i],dt.conf2[i]);  //  Evaluate nearest neighbour classifier
+      dt.patt[i]=tmp.patt[idx];
+      //printf("Testing feature %d, conf:%f isin:(%d|%d|%d)\n",i,dt.conf1[i],dt.isin[i][0],dt.isin[i][1],dt.isin[i][2]);
+      if (dt.conf1[i]>nn_th){                                               //  idx = dt.conf1 > tld.model.thr_nn; % get all indexes that made it through the nearest neighbour
+          dbb.push_back(grid[idx]);                                         //  BB    = dt.bb(:,idx); % bounding boxes
+          dconf.push_back(dt.conf2[i]);                                     //  Conf  = dt.conf2(:,idx); % conservative confidences
+      }
+  }                                                                         //  end
+  if (dbb.size()>0){
+      printf("Found %d NN matches\n",(int)dbb.size());
+      detected=true;
+  }
+  else{
+      printf("No NN matches found.\n");
+      detected=false;
+  }
+}
+
+void TLD::evaluate(){
+}
+
+void TLD::learn(const Mat& img){
+  printf("[Learning] ");
+  ///Check consistency
+  BoundingBox bb;
+  bb.x = max(lastbox.x,0);
+  bb.y = max(lastbox.y,0);
+  bb.width = min(min(img.cols-lastbox.x,lastbox.width),min(lastbox.width,lastbox.br().x));
+  bb.height = min(min(img.rows-lastbox.y,lastbox.height),min(lastbox.height,lastbox.br().y));
+  Scalar mean, stdev;
+  Mat pattern;
+  getPattern(img(bb),pattern,mean,stdev);
+  vector<int> isin;
+  float dummy, conf;
+  classifier.NNConf(pattern,isin,conf,dummy);
+  if (conf<0.5) {
+      printf("Fast change..not training\n");
+      lastvalid =false;
+      return;
+  }
+  if (pow(stdev.val[0],2)<var){
+      printf("Low variance..not training\n");
+      lastvalid=false;
+      return;
+  }
+  if(isin[2]==1){
+      printf("Patch in negative data..not traing");
+      lastvalid=false;
+      return;
+  }
+/// Data generation
+  for (int i=0;i<grid.size();i++){
+      grid[i].overlap = bbOverlap(lastbox,grid[i]);
+  }
+  vector<pair<vector<int>,int> > fern_examples;
+  good_boxes.clear();
+  bad_boxes.clear();
+  getOverlappingBoxes(lastbox,num_closest_update);
+  if (good_boxes.size()>0)
+    generatePositiveData(img,num_warps_update);
+  else{
+    lastvalid = false;
+    printf("No good boxes..Not training");
+    return;
+  }
+  fern_examples.reserve(pX.size()+bad_boxes.size());
+  fern_examples.assign(pX.begin(),pX.end());
+  int idx;
+  for (int i=0;i<bad_boxes.size();i++){
+      idx=bad_boxes[i];
+      if (tmp.conf[idx]>=1){
+          fern_examples.push_back(make_pair(tmp.patt[idx],0));
+      }
+  }
+  vector<Mat> nn_examples;
+  nn_examples.reserve(dt.bb.size()+1);
+  nn_examples.push_back(pEx);
+  for (int i=0;i<dt.bb.size();i++){
+      idx = dt.bb[i];
+      if (bbOverlap(lastbox,grid[idx]) < bad_overlap)
+        nn_examples.push_back(dt.patch[i]);
+  }
+  /// Classifiers update
+  classifier.trainF(fern_examples,2);
+  classifier.trainNN(nn_examples);
+  classifier.show();
+}
+
+void TLD::buildGrid(const cv::Mat& img, const cv::Rect& box){
+  const float SHIFT = 0.1;
+  const float SCALES[] = {0.16151,0.19381,0.23257,0.27908,0.33490,0.40188,0.48225,
+                          0.57870,0.69444,0.83333,1,1.20000,1.44000,1.72800,
+                          2.07360,2.48832,2.98598,3.58318,4.29982,5.15978,6.19174};
+  int width, height, min_bb_side;
+  //Rect bbox;
+  BoundingBox bbox;
+  Size scale;
+  int sc=0;
+  for (int s=0;s<21;s++){
+    width = round(box.width*SCALES[s]);
+    height = round(box.height*SCALES[s]);
+    min_bb_side = min(height,width);
+    if (min_bb_side < min_win || width > img.cols || height > img.rows)
+      continue;
+    scale.width = width;
+    scale.height = height;
+    scales.push_back(scale);
+    for (int y=1;y<img.rows-height;y+=round(SHIFT*min_bb_side)){
+      for (int x=1;x<img.cols-width;x+=round(SHIFT*min_bb_side)){
+        bbox.x = x;
+        bbox.y = y;
+        bbox.width = width;
+        bbox.height = height;
+        bbox.overlap = bbOverlap(bbox,BoundingBox(box));
+        bbox.sidx = sc;
+        grid.push_back(bbox);
+      }
+    }
+    sc++;
+  }
+}
+
+float TLD::bbOverlap(const BoundingBox& box1,const BoundingBox& box2){
+  if (box1.x > box2.x+box2.width) { return 0.0; }
+  if (box1.y > box2.y+box2.height) { return 0.0; }
+  if (box1.x+box1.width < box2.x) { return 0.0; }
+  if (box1.y+box1.height < box2.y) { return 0.0; }
+
+  float colInt =  min(box1.x+box1.width,box2.x+box2.width) - max(box1.x, box2.x);
+  float rowInt =  min(box1.y+box1.height,box2.y+box2.height) - max(box1.y,box2.y);
+
+  float intersection = colInt * rowInt;
+  float area1 = box1.width*box1.height;
+  float area2 = box2.width*box2.height;
+  return intersection / (area1 + area2 - intersection);
+}
+
+void TLD::getOverlappingBoxes(const cv::Rect& box1,int num_closest){
+  float max_overlap = 0;
+  for (int i=0;i<grid.size();i++){
+      if (grid[i].overlap > max_overlap) {
+          max_overlap = grid[i].overlap;
+          best_box = grid[i];
+      }
+      if (grid[i].overlap > 0.6){
+          good_boxes.push_back(i);
+      }
+      else if (grid[i].overlap < bad_overlap){
+          bad_boxes.push_back(i);
+      }
+  }
+  //Get the best num_closest (10) boxes and puts them in good_boxes
+  if (good_boxes.size()>num_closest){
+    std::nth_element(good_boxes.begin(),good_boxes.begin()+num_closest,good_boxes.end(),OComparator(grid));
+    good_boxes.resize(num_closest);
+  }
+  getBBHull();
+}
+
+void TLD::getBBHull(){
+  int x1=INT_MAX, x2=0;
+  int y1=INT_MAX, y2=0;
+  int idx;
+  for (int i=0;i<good_boxes.size();i++){
+      idx= good_boxes[i];
+      x1=min(grid[idx].x,x1);
+      y1=min(grid[idx].y,y1);
+      x2=max(grid[idx].x+grid[idx].width,x2);
+      y2=max(grid[idx].y+grid[idx].height,y2);
+  }
+  bbhull.x = x1;
+  bbhull.y = y1;
+  bbhull.width = x2-x1;
+  bbhull.height = y2 -y1;
+}
+
+bool bbcomp(const BoundingBox& b1,const BoundingBox& b2){
+  TLD t;
+    if (t.bbOverlap(b1,b2)<0.5)
+      return false;
+    else
+      return true;
+}
+int TLD::clusterBB(const vector<BoundingBox>& dbb,vector<int>& indexes){
+  //FIXME: Conditional jump or move depends on uninitialised value(s)
+  const int c = dbb.size();
+  //1. Build proximity matrix
+  Mat D(c,c,CV_32F);
+  float d;
+  for (int i=0;i<c;i++){
+      for (int j=i+1;j<c;j++){
+        d = 1-bbOverlap(dbb[i],dbb[j]);
+        D.at<float>(i,j) = d;
+        D.at<float>(j,i) = d;
+      }
+  }
+  //2. Initialize disjoint clustering
+ float L[c-1]; //Level
+ int nodes[c-1][2];
+ int belongs[c];
+ int m=c;
+ for (int i=0;i<c;i++){
+    belongs[i]=i;
+ }
+ for (int it=0;it<c-1;it++){
+ //3. Find nearest neighbor
+     float min_d = 1;
+     int node_a, node_b;
+     for (int i=0;i<D.rows;i++){
+         for (int j=i+1;j<D.cols;j++){
+             if (D.at<float>(i,j)<min_d && belongs[i]!=belongs[j]){
+                 min_d = D.at<float>(i,j);
+                 node_a = i;
+                 node_b = j;
+             }
+         }
+     }
+     if (min_d>0.5){
+         int max_idx =0;
+         bool visited;
+         for (int j=0;j<c;j++){
+             visited = false;
+             for(int i=0;i<2*c-1;i++){
+                 if (belongs[j]==i){
+                     indexes[j]=max_idx;
+                     visited = true;
+                 }
+             }
+             if (visited)
+               max_idx++;
+         }
+         return max_idx;
+     }
+
+ //4. Merge clusters and assign level
+     L[m]=min_d;
+     nodes[it][0] = belongs[node_a];
+     nodes[it][1] = belongs[node_b];
+     for (int k=0;k<c;k++){
+         if (belongs[k]==belongs[node_a] || belongs[k]==belongs[node_b])
+           belongs[k]=m;
+     }
+     m++;
+ }
+ return 1;
+
+}
+
+void TLD::clusterConf(const vector<BoundingBox>& dbb,const vector<float>& dconf,vector<BoundingBox>& cbb,vector<float>& cconf){
+  int numbb =dbb.size();
+  vector<int> T;
+  float space_thr = 0.5;
+  int c=1;
+  switch (numbb){
+  case 1:
+    cbb=vector<BoundingBox>(1,dbb[0]);
+    cconf=vector<float>(1,dconf[0]);
+    return;
+    break;
+  case 2:
+    T =vector<int>(2,0);
+    if (1-bbOverlap(dbb[0],dbb[1])>space_thr){
+      T[1]=1;
+      c=2;
+    }
+    break;
+  default:
+    T = vector<int>(numbb,0);
+    c = partition(dbb,T,(&bbcomp));
+    //c = clusterBB(dbb,T);
+    break;
+  }
+  cconf=vector<float>(c);
+  cbb=vector<BoundingBox>(c);
+  printf("Cluster indexes: ");
+  BoundingBox bx;
+  for (int i=0;i<c;i++){
+      float cnf=0;
+      int N=0,mx=0,my=0,mw=0,mh=0;
+      for (int j=0;j<T.size();j++){
+          if (T[j]==i){
+              printf("%d ",i);
+              cnf=cnf+dconf[j];
+              mx=mx+dbb[j].x;
+              my=my+dbb[j].y;
+              mw=mw+dbb[j].width;
+              mh=mh+dbb[j].height;
+              N++;
+          }
+      }
+      if (N>0){
+          cconf[i]=cnf/N;
+          bx.x=cvRound(mx/N);
+          bx.y=cvRound(my/N);
+          bx.width=cvRound(mw/N);
+          bx.height=cvRound(mh/N);
+          cbb[i]=bx;
+      }
+  }
+  printf("\n");
+}
diff --git a/src/run_tld.cpp b/src/run_tld.cpp
new file mode 100755
index 0000000..7979f59
--- /dev/null
+++ b/src/run_tld.cpp
@@ -0,0 +1,206 @@
+#include <opencv2/opencv.hpp>
+#include <tld_utils.h>
+#include <iostream>
+#include <sstream>
+#include <TLD.h>
+#include <stdio.h>
+using namespace cv;
+using namespace std;
+//Global variables
+Rect box;
+bool drawing_box = false;
+bool gotBB = false;
+bool tl = true;
+bool rep = false;
+bool fromfile=false;
+string video;
+
+void readBB(char* file){
+  ifstream bb_file (file);
+  string line;
+  getline(bb_file,line);
+  istringstream linestream(line);
+  string x1,y1,x2,y2;
+  getline (linestream,x1, ',');
+  getline (linestream,y1, ',');
+  getline (linestream,x2, ',');
+  getline (linestream,y2, ',');
+  int x = atoi(x1.c_str());// = (int)file["bb_x"];
+  int y = atoi(y1.c_str());// = (int)file["bb_y"];
+  int w = atoi(x2.c_str())-x;// = (int)file["bb_w"];
+  int h = atoi(y2.c_str())-y;// = (int)file["bb_h"];
+  box = Rect(x,y,w,h);
+}
+//bounding box mouse callback
+void mouseHandler(int event, int x, int y, int flags, void *param){
+  switch( event ){
+  case CV_EVENT_MOUSEMOVE:
+    if (drawing_box){
+        box.width = x-box.x;
+        box.height = y-box.y;
+    }
+    break;
+  case CV_EVENT_LBUTTONDOWN:
+    drawing_box = true;
+    box = Rect( x, y, 0, 0 );
+    break;
+  case CV_EVENT_LBUTTONUP:
+    drawing_box = false;
+    if( box.width < 0 ){
+        box.x += box.width;
+        box.width *= -1;
+    }
+    if( box.height < 0 ){
+        box.y += box.height;
+        box.height *= -1;
+    }
+    gotBB = true;
+    break;
+  }
+}
+
+void print_help(char** argv){
+  printf("use:\n     %s -p /path/parameters.yml\n",argv[0]);
+  printf("-s    source video\n-b        bounding box file\n-tl  track and learn\n-r     repeat\n");
+}
+
+void read_options(int argc, char** argv,VideoCapture& capture,FileStorage &fs){
+  for (int i=0;i<argc;i++){
+      if (strcmp(argv[i],"-b")==0){
+          if (argc>i){
+              readBB(argv[i+1]);
+              gotBB = true;
+          }
+          else
+            print_help(argv);
+      }
+      if (strcmp(argv[i],"-s")==0){
+          if (argc>i){
+              video = string(argv[i+1]);
+              capture.open(video);
+              fromfile = true;
+          }
+          else
+            print_help(argv);
+
+      }
+      if (strcmp(argv[i],"-p")==0){
+          if (argc>i){
+              fs.open(argv[i+1], FileStorage::READ);
+          }
+          else
+            print_help(argv);
+      }
+      if (strcmp(argv[i],"-no_tl")==0){
+          tl = false;
+      }
+      if (strcmp(argv[i],"-r")==0){
+          rep = true;
+      }
+  }
+}
+
+int main(int argc, char * argv[]){
+  VideoCapture capture;
+  capture.open(0);
+  FileStorage fs;
+  //Read options
+  read_options(argc,argv,capture,fs);
+  //Init camera
+  if (!capture.isOpened())
+  {
+	cout << "capture device failed to open!" << endl;
+    return 1;
+  }
+  //Register mouse callback to draw the bounding box
+  cvNamedWindow("TLD",CV_WINDOW_AUTOSIZE);
+  cvSetMouseCallback( "TLD", mouseHandler, NULL );
+  //TLD framework
+  TLD tld;
+  //Read parameters file
+  tld.read(fs.getFirstTopLevelNode());
+  Mat frame;
+  Mat last_gray;
+  Mat first;
+  if (fromfile){
+      capture >> frame;
+      cvtColor(frame, last_gray, CV_RGB2GRAY);
+      frame.copyTo(first);
+  }else{
+      capture.set(CV_CAP_PROP_FRAME_WIDTH,640);
+      capture.set(CV_CAP_PROP_FRAME_HEIGHT,480);
+  }
+
+  ///Initialization
+GETBOUNDINGBOX:
+  while(!gotBB)
+  {
+    if (!fromfile){
+      capture >> frame;
+    }
+    else
+      first.copyTo(frame);
+    cvtColor(frame, last_gray, CV_RGB2GRAY);
+    drawBox(frame,box);
+    imshow("TLD", frame);
+    if (cvWaitKey(33) == 'q')
+	    return 0;
+  }
+  if (min(box.width,box.height)<(int)fs.getFirstTopLevelNode()["min_win"]){
+      cout << "Bounding box too small, try again." << endl;
+      gotBB = false;
+      goto GETBOUNDINGBOX;
+  }
+  //Remove callback
+  cvSetMouseCallback( "TLD", NULL, NULL );
+  printf("Initial Bounding Box = x:%d y:%d h:%d w:%d\n",box.x,box.y,box.width,box.height);
+  //Output file
+  FILE  *bb_file = fopen("bounding_boxes.txt","w");
+  //TLD initialization
+  tld.init(last_gray,box,bb_file);
+
+  ///Run-time
+  Mat current_gray;
+  BoundingBox pbox;
+  vector<Point2f> pts1;
+  vector<Point2f> pts2;
+  bool status=true;
+  int frames = 1;
+  int detections = 1;
+REPEAT:
+  while(capture.read(frame)){
+    //get frame
+    cvtColor(frame, current_gray, CV_RGB2GRAY);
+    //Process Frame
+    tld.processFrame(last_gray,current_gray,pts1,pts2,pbox,status,tl,bb_file);
+    //Draw Points
+    if (status){
+      drawPoints(frame,pts1);
+      drawPoints(frame,pts2,Scalar(0,255,0));
+      drawBox(frame,pbox);
+      detections++;
+    }
+    //Display
+    imshow("TLD", frame);
+    //swap points and images
+    swap(last_gray,current_gray);
+    pts1.clear();
+    pts2.clear();
+    frames++;
+    printf("Detection rate: %d/%d\n",detections,frames);
+    if (cvWaitKey(33) == 'q')
+      break;
+  }
+  if (rep){
+    rep = false;
+    tl = false;
+    fclose(bb_file);
+    bb_file = fopen("final_detector.txt","w");
+    //capture.set(CV_CAP_PROP_POS_AVI_RATIO,0);
+    capture.release();
+    capture.open(video);
+    goto REPEAT;
+  }
+  fclose(bb_file);
+  return 0;
+}
diff --git a/src/tld_utils.cpp b/src/tld_utils.cpp
new file mode 100644
index 0000000..050b0eb
--- /dev/null
+++ b/src/tld_utils.cpp
@@ -0,0 +1,38 @@
+#include <tld_utils.h>
+using namespace cv;
+using namespace std;
+
+void drawBox(Mat& image, CvRect box, Scalar color, int thick){
+  rectangle( image, cvPoint(box.x, box.y), cvPoint(box.x+box.width,box.y+box.height),color, thick);
+} 
+
+void drawPoints(Mat& image, vector<Point2f> points,Scalar color){
+  for( vector<Point2f>::const_iterator i = points.begin(), ie = points.end(); i != ie; ++i )
+      {
+      Point center( cvRound(i->x ), cvRound(i->y));
+      circle(image,*i,2,color,1);
+      }
+}
+
+Mat createMask(const Mat& image, CvRect box){
+  Mat mask = Mat::zeros(image.rows,image.cols,CV_8U);
+  drawBox(mask,box,Scalar::all(255),CV_FILLED);
+  return mask;
+}
+
+float median(vector<float> v)
+{
+    int n = floor(v.size() / 2);
+    nth_element(v.begin(), v.begin()+n, v.end());
+    return v[n];
+}
+
+vector<int> index_shuffle(int begin,int end){
+  vector<int> indexes(end-begin);
+  for (int i=begin;i<end;i++){
+    indexes[i]=i;
+  }
+  random_shuffle(indexes.begin(),indexes.end());
+  return indexes;
+}
+