c-peng/dnt

Dual Deep Network for Visual Tracking

Dual Deep Network for Visual Tracking

Introduction

DNT repository for Dual Deep Network for Visual Tracking is published in IEEE Transaction on Image Processing [IEEE Xplore] [arXiv]. This package contains the source code to reproduce the experimental results of DNT paper. The source code is mainly written in MATLAB.

There a tracking benchmark tracking repo. Check them out!

Usage

• Supported OS: the source code was tested on 64-bit Arch Linux OS, and it should also be executable in other linux distributions.

• Dependencies:

  • Deep learning framework caffe and all its dependencies.
  • Cuda-enabled GPUs.

• Installation:

  • Install caffe: caffe is our customized version of the original caffe. Change directory into ./caffe and compile the source code and the matlab interface following the installation instruction of caffe.

  • Download the 16-layer VGG network from Simonyan's gist, and put the caffemodel file under the ./feature_model directory.

  • Run the demo code run_tracker.m. You can customize your own test sequences following the example inside.

Having questions?

Feel free to contact us!

Citing Our Work

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

@article{chi2017_tracking,
   title={Dual Deep Network for Visual Tracking},
   author={Chi, Zhizhen and Li, Hongyang and Lu, Huchuan and Yang, Minghsuan},
   volume={26},
   issue={4},
   pages={2005-2015},
   journal={IEEE Transaction on Image Processing},
   year={2017}
}