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Visual Tracking with Convolutional Random Vector Functional Link Network
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Benchmark_CVPR13_results
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CRVFLEn.m
README.md
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README.md

CRVFLEn

Zhang Le, P.N. Suganthan, ''Visual Tracking with Convolutional Random Vector Functional Link Network" , IEEE Trans. On Cybernetics

This software is an imaplentation of the tracking method described in Visual Tracking with Convolutional Random Vector Functional Link Network.

The code is based on the following work:

MEEM: Robust Tracking via Multiple Experts using Entropy Minimization", Jianming Zhang, Shugao Ma, Stan Sclaroff, ECCV, 2014.

The code is maintained by Zhang Le. If you have questions, please contact zhang.le@adsc.com.sg/lzhang027@e.ntu.edu.sg

June. 2016

This code has been tested on 64-bit Windows with OpenCV 2.40+ on CPU. We have not tested it on GPU but it is easy to extend based on the current implementation.

Installation:

  1. You should have OpenCV 2.40+ and MatConvNet (we use 1.0-beta7) installed.
  2. Unzip the files to <install_dir>.
  3. Launch Matlab.
  4. Go to <install_dir>\mex, and open "compile.m".
  5. Change the OpenCV inlude and lib directory to yours, ans save.
  6. run "compile" in Matlab.
  7. Go back to <install_dir>, and run "demo".

How to use:

Just insert it into the visual tracking benchmark:

Wu, Yi, Jongwoo Lim, and Ming-Hsuan Yang. "Online object tracking: A benchmark." Proceedings of the IEEE conference on computer vision and pattern recognition. 2013.

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