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:

1. 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.
2. Download the 16-layer VGG network from https://gist.github.com/ksimonyan/211839e770f7b538e2d8, and put the caffemodel file under the ./model directory.
3. 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
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