Kernelized Correlation filters(KCF)

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.