-
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.
- Download imagenet-vgg-m-2048 from http://www.vlfeat.org/matconvnet/pretrained/, and put the file into ./networks
- Compile matconvnet in the sub-folders.
- Run the demo code demo_DRT.m. You can customize your own test sequences following this example.
The tracking results may be a little different on different machines. The suggested MATLAB and CUDA versions are MATLAB R2014B and CUDA 8.0.
If you find our paper useful, please consider citing it. @inproceedings{sun2018correlation, title={Correlation Tracking via Joint Discrimination and Reliability Learning}, author={Sun, Chong and Wang, Dong and Lu, Huchuan and Yang, Ming-Hsuan}, booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition}, pages={489--497}, year={2018} }