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readme.txt
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High-Speed Tracking with Kernelized Correlation Filters
J. F. Henriques R. Caseiro P. Martins J. Batista
TPAMI 2014
________________
To be published.
arXiv pre-print: http://arxiv.org/abs/1404.7584
Project webpage: http://www.isr.uc.pt/~henriques/circulant/
This MATLAB code implements a simple tracking pipeline based on the Kernelized
Correlation Filter (KCF), and Dual Correlation Filter (DCF).
It is free for research use. If you find it useful, please acknowledge the paper
above with a reference.
__________
Quickstart
1. Extract code somewhere.
2. The tracker is prepared to run on any of the 50 videos of the Visual Tracking
Benchmark [3]. For that, it must know where they are/will be located. You can
change the default location 'base_path' in 'download_videos.m' and 'run_tracker.m'.
3. If you don't have the videos already, run 'download_videos.m' (may take some time).
4. Execute 'run_tracker' without parameters to choose a video and test the KCF on it.
Note: The tracker uses the 'fhog'/'gradientMex' functions from Piotr's Toolbox.
Some pre-compiled MEX files are provided for convenience. If they do not work for your
system, just get the toolbox from http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
__________
The main interface function is 'run_tracker'. You can test several configurations (KCF,
DCF, MOSSE) by calling it with different commands:
run_tracker
Without any parameters, will ask you to choose a video, track using
the Gaussian KCF on HOG, and show the results in an interactive
figure. Press 'Esc' to stop the tracker early. You can navigate the
video using the scrollbar at the bottom.
run_tracker VIDEO
Allows you to select a VIDEO by its name. 'all' will run all videos
and show average statistics. 'choose' will select one interactively.
run_tracker VIDEO KERNEL
Choose a KERNEL. 'gaussian'/'polynomial' to run KCF, 'linear' for DCF.
run_tracker VIDEO KERNEL FEATURE
Choose a FEATURE type, either 'hog' or 'gray' (raw pixels).
run_tracker(VIDEO, KERNEL, FEATURE, SHOW_VISUALIZATION, SHOW_PLOTS)
Decide whether to show the scrollable figure, and the precision plot.
Useful combinations:
>> run_tracker choose gaussian hog %Kernelized Correlation Filter (KCF)
>> run_tracker choose linear hog %Dual Correlation Filter (DCF)
>> run_tracker choose gaussian gray %Single-channel KCF (ECCV'12 paper)
>> run_tracker choose linear gray %MOSSE filter (single channel)
For the actual tracking code, check out the 'tracker' function.
Though it's not required, the code will make use of the MATLAB Parallel Computing
Toolbox automatically if available.
__________
References
[1] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "High-Speed Tracking with
Kernelized Correlation Filters", TPAMI 2014 (to be published).
[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "Exploiting the Circulant
Structure of Tracking-by-detection with Kernels", ECCV 2012.
[3] Y. Wu, J. Lim, M.-H. Yang, "Online Object Tracking: A Benchmark", CVPR 2013.
Website: http://visual-tracking.net/
[4] P. Dollar, "Piotr's Image and Video Matlab Toolbox (PMT)".
Website: http://vision.ucsd.edu/~pdollar/toolbox/doc/index.html
[5] P. Dollar, S. Belongie, P. Perona, "The Fastest Pedestrian Detector in the
West", BMVC 2010.
_____________________________________
Copyright (c) 2014, Joao F. Henriques
Permission to use, copy, modify, and distribute this software for research
purposes with or without fee is hereby granted, provided that the above
copyright notice and this permission notice appear in all copies.
THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES
WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR
ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES
WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN
ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF
OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.