Overview

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Code for Sparse Kernel Tracking

Rui.Yao, 28/06/2012.

This code accompanies the paper:
Rui Yao, Shixiong Xia, and Yong Zhou. 
Robust Tracking via Online Max-Margin Structural Learning with Approximate Sparse Intersection Kernel.
Neurocomputing 157 (2015): 344-355.

Contact: Rui Yao (ruiyao@cumt.edu.cn)

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License
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  THIS SOFTWARE IS PROVIDED BY LU ZHANG AND AURENS VAN DER MAATEN ''AS IS'' AND ANY EXPRESS
  OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES 
  OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO 
  EVENT SHALL LU ZHANG AND LAURENS VAN DER MAATEN BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, 
  SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, 
  PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR 
  BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN 
  CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING 
  IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY 
  OF SUCH DAMAGE.


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Citations
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In case you use SKT code in your work, please cite the following paper:

@article{yao2015robust,
  title={Robust tracking via online Max-Margin structural learning with approximate sparse intersection kernel},
  author={Yao, Rui and Xia, Shixiong and Zhou, Yong},
  journal={Neurocomputing},
  volume={157},
  pages={344--355},
  year={2015},
  publisher={Elsevier}
}

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Requirements
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This code has been developed and tested with Ubuntu 11.04, Matlab R2012a (64-bit).

The files require several opensource libraries to run
1) Stocastic gradient descent algorithm for training models with the    
   modified regularization. 
2) mex (see berkeley BSDS library). The directory
   contains precompiled mex code for linux (32&64 bit machines)

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Usage
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>> SparseKernelTracking