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Project webpage: http://arxiv.org/pdf/1601.06032.pdf

This MATLAB code implements the tracking pipeline based on the SCF, MSCF, KSCF and SKSCF.
SCF: support correlation filter
MSCF: multi-channel support correlaiton filter
KSCF: kernel support correlation filter
SKSCF: scale-adaptive correlation filter

The initializaiton of filter has a great influence on the tracking performance.
Thus we provide 2 version of the code: the one-iteration version and the optimal version.
One-iteration version is the simplified version by using the initialization of filter with 'params.maxiter = 1' in makeParams.m;
and the optimal version is the one with the optimal number of iterations when learning the filter. 
The optimal iterations range from 5 to 10 in 'MSCF.m', 'KSCF.m' and 'SKSCF.m'.

Note：
SCF is the single-channel model, whose implementation is inlcuded in the MSCF with the 'feature_type' variable to 'gray'.

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Quickstart

1. Extract code somewhere.

2. The tracker is available for the 50 videos of the Visual Tracking
   Benchmark [3]. You need to put all videos under the default location 'base_path' in 'run.m'.

3. The videos can be downloaded from http://cvlab.hanyang.ac.kr/tracker_benchmark/datasets.html.

4. Execute 'run' without parameters to test on it. For SCF, do not forget to change the 'feature_type' variable to 'gray'.


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

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References

[1] W. Zuo, X. Wu, L. Lin, L. Zhang, M.-H Yang. "Learning Support Correlation 
Filters for Visual Tracking", arXiv preprint arXiv:1601.06032, 2016.

[2] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "High-Speed Tracking with
Kernelized Correlation Filters", TPAMI 2014.

[3] J. F. Henriques, R. Caseiro, P. Martins, J. Batista, "Exploiting the Circulant
Structure of Tracking-by-detection with Kernels", ECCV 2012.

[4] Y. Wu, J. Lim, M.-H. Yang, "Online Object Tracking: A Benchmark", CVPR 2013.
Website: http://visual-tracking.net/

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Citation

If you find the code and dataset useful in your research, please consider citing:

@article{zuo2016learning,
  title={Learning Support Correlation Filters for Visual Tracking},
  author={Zuo, Wangmeng and Wu, Xiaohe and Lin, Liang and Zhang, Lei and Yang, Ming-Hsuan},
  journal={arXiv preprint arXiv:1601.06032},
  year={2016}
}

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Contents

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Feedbacks and comments are welcome! Feel free to contact us via [lotuswxh@gmail.com] or [angela612@126.com].

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Liscense
Copyright (c) 2016, Xiaohe Wu
All rights reserved. 
Redistribution and use in source and binary forms, with or without modification, are 
permitted provided that the following conditions are met:
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  notice, this list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright 
  notice, this list of conditions and the following disclaimer in 
  the documentation and/or other materials provided with the distribution
        
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