Update README.md

Author: joaofaro

README.md

@@ -1,69 +1,48 @@
 # C++ KCF Tracker
-This package includes a C++ class with several tracking methods based on the Kernelized Correlation Filter (KCF) [1, 2].
-
+This package includes a C++ class with several tracking methods based on the Kernelized Correlation Filter (KCF) [1, 2].   
 It also includes an executable to interface with the VOT benchmark.
 
-
-[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
+[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,   
 "High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.
 
-[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,
+[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista,   
 "Exploiting the Circulant Structure of Tracking-by-detection with Kernels", ECCV 2012.
 
 
-Authors: Joao Faro, Christian Bailer, Joao F. Henriques
-
-Contacts: [email protected], [email protected], [email protected]
-
-Institute of Systems and Robotics - University of Coimbra / Department of Augmented Vision DFKI
-
+Authors: Joao Faro, Christian Bailer, Joao F. Henriques   
+Contacts: [email protected], [email protected], [email protected]   
+Institute of Systems and Robotics - University of Coimbra / Department of Augmented Vision DFKI   
 
 ### Algorithms (in this folder) ###
 
-"KCFC++", command: ./KCF
-
+"KCFC++", command: ./KCF   
 Description: KCF on HOG features, ported to C++ OpenCV. The original Matlab tracker placed 3rd in VOT 2014.
 
+"KCFLabC++", command: ./KCF lab   
+Description: KCF on HOG and Lab features, ported to C++ OpenCV. The Lab features are computed by quantizing CIE-Lab colors into 15 centroids, obtained from natural images by k-means.   
 
-"KCFLabC++", command: ./KCF lab
-
-Description: KCF on HOG and Lab features, ported to C++ OpenCV. The Lab features are computed by quantizing CIE-Lab colors into 15 centroids, obtained from natural images by k-means.
-
-
-The CSK tracker [2] is also implemented as a bonus, simply by using raw grayscale as features (the filter becomes single-channel).
-
+The CSK tracker [2] is also implemented as a bonus, simply by using raw grayscale as features (the filter becomes single-channel).   
 
 ### Compilation instructions ###
-There are no external dependencies other than OpenCV 3.0.0. Tested on a freshly installed Ubuntu 14.04.
-
-1) cmake CMakeLists.txt
-
-2) make
+There are no external dependencies other than OpenCV 3.0.0. Tested on a freshly installed Ubuntu 14.04.   
 
+1) cmake CMakeLists.txt   
+2) make   
 
 ### Running instructions ###
 
-The runtracker.cpp is prepared to be used with the VOT toolkit. The executable "KCF" should be called as:
-
+The runtracker.cpp is prepared to be used with the VOT toolkit. The executable "KCF" should be called as:   
 
 ./KCF [OPTION_1] [OPTION_2] [...]
 
+Options available:   
 
-Options available:
-
-
-gray - Use raw gray level features as in [1].
-
-hog - Use HOG features as in [2].
-
-lab - Use Lab colorspace features. This option will also enable HOG features by default.
-
-singlescale - Performs single-scale detection, using a variable-size window.
-
-fixed_window - Keep the window size fixed when in single-scale mode (multi-scale always used a fixed window).
-
-show - Show the results in a window.
-
+gray - Use raw gray level features as in [1].   
+hog - Use HOG features as in [2].   
+lab - Use Lab colorspace features. This option will also enable HOG features by default.   
+singlescale - Performs single-scale detection, using a variable-size window.   
+fixed_window - Keep the window size fixed when in single-scale mode (multi-scale always used a fixed window).   
+show - Show the results in a window.   
 
 To include it in your project, without the VOT toolkit you just need to: