C++ KCF Tracker

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,
"High-Speed Tracking with Kernelized Correlation Filters", TPAMI 2015.

[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: joaopfaro@gmail.com, Christian.Bailer@dfki.de, henriques@isr.uc.pt
Institute of Systems and Robotics - University of Coimbra / Department of Augmented Vision DFKI

Algorithms (in this folder)

"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.

The CSK tracker [2] is also implemented as a bonus, simply by using raw grayscale as features (the filter becomes single-channel).

Running instructions

Data

The directory bin/JPEGImages contains all frame images named by their indices (start from 1). The file bin/config.txt stores the configurations of the tracker:

120 human 205 151 222 201

• "120": Frames amount.
• "human": Label name.
• "205 151 222 201": Bounding box (left-top coordinate + right-bottom coordinate) of the first frame.

Compile and run

There are no external dependencies other than OpenCV 3.0.0+. Tested on freshly installed Ubuntu 14.04 / Ubuntu 16.04.

$ mkdir build
$ cd build
$ cmake ..
$ make
$ cd ../bin
$ ./KCF

The bounding box of each frame will be stored to a file named result.txt:

1.jpg human 205 151 222 201
2.jpg human 204 150 221 200
...
120.jpg human 58 93 71 132

For each row:

• "1.jpg": Frame name.
• "human": Label name.
• "205 151 222 201": Bounding box of the frame.

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Original instructions

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:

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:

// Create the KCFTracker object with one of the available options
KCFTracker tracker(HOG, FIXEDWINDOW, MULTISCALE, LAB);

// Give the first frame and the position of the object to the tracker
tracker.init( Rect(xMin, yMin, width, height), frame );

// Get the position of the object for the new frame
result = tracker.update(frame);