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STCT: Sequentially Training Convolutional Networks for Visual Tracking

Introduction

STCT is an online visual tracking algorithm by sequentailly training convolutional neural networks. This package contains the source code to reproduce the experimental results of STCT reported in our CVPR 2016 paper. The source code is mainly written in MATLAB with .

Usage

Supported OS: the source code was tested on 64-bit Arch and Ubuntu 14.04 Linux OS, and it should also be executable in other linux distributions.
Dependencies:
A modified version of caffe framework and all its dependencies.
Cuda enabled GPUs
Installation:

Install caffe: we use a modified version of the original caffe framework. Compile the source code in the ./caffe directory and the matlab interface following the installation instruction of caffe.
Download the 16-layer VGG network from https://gist.github.com/ksimonyan/211839e770f7b538e2d8, and put the caffemodel file under the ./model directory.
Run the demo code demo_STCT.m. You can customize your own test sequences following this example.

Citing Our Work

If you find STCT useful in your research, please consider to cite our paper:

    @inproceedings{wang2016STCT,
       title={STCT: Sequentially Training Convolutional Networks for Visual Tracking},
       author={Wang, Lijun and Ouyang, Wanli and Wang, Xiaogang and Lu, Huchuan},
       booktitle={CVPR},
       year={2016}
    }

Liscense

    Copyright (c) 2016, Lijun Wang
    All rights reserved. 
    Redistribution and use in source and binary forms, with or without modification, are 
    permitted provided that the following conditions are met:
		* Redistributions of source code must retain the above copyright 
  		  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
    
    THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 
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