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caffe
ccot_runfile
clustering
external_libs original Aug 5, 2018
libsvm
model original Aug 5, 2018
util original Aug 5, 2018
.gitignore
DRT.m
README.md
README.md~ original Aug 5, 2018
compute_right_hand.m
demo_DRT.m
extract_vgg16.m
feature_reduction.m
feature_reduction.m~
gaussian_shaped_labels.m
get_binary_patch_mask.m
imResampleMex.mexa64
impreprocess.m original Aug 5, 2018
matlab_validation1.m
model_update.m
process_freq.m
process_freq1.m original Aug 5, 2018
process_hf.m original Aug 5, 2018
set_tracker_param.m
set_tracker_param1.m

README.md

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. Download imagenet-vgg-m-2048 from http://www.vlfeat.org/matconvnet/pretrained/, and put the file into ./networks
  4. Compile matconvnet in the sub-folders.
  5. 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} }

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