Code for tracker in the paper Multi-Cue Correlation Filters for Robust Visual Tracking, by Ning Wang, Wengang Zhou et. al - to appear in CVPR 2018.
In this work, we propose to utilize multiple weak experts for online tracking. Our efficient framework achieves state-of-the-art performance just using standard DCFs !
For questions about the code or paper, please feel free to contact me: [email protected]
If you find MCCT useful in your research, please consider citing:
@InProceedings{NingCVPR2018,
Title = {Multi-Cue Correlation Filters for Robust Visual Tracking},
Author = {Ning Wang, Wengang Zhou, Qi Tian, Richang Hong, Meng Wang, Houqiang Li},
Booktitle = {CVPR},
Year = {2018}
}
For the MCCT-H (Hand-crafted features only) tracker, just start Matlab and run the runTracker.m
. To run the MCCT tracker with deep features, please download the VGG-19 and compile the Matconvnet following the description in README (in MCCT/model/
).
- The VGG-19 model is available at http://www.vlfeat.org/matconvnet/pretrained/.
- The Matconvnet is available at https://github.com/vlfeat/matconvnet.
- The code is mostly in MATLAB, except the workhorse of
fhog.m
, which is written in C and comes from Piotr Dollar toolbox http://vision.ucsd.edu/~pdollar/toolbox - gradientMex and mexResize have been compiled and tested for Ubuntu and Windows 8 (64 bit). You can easily recompile the sources in case of need.
Some codes of this work are adopted from previous trackers (Staple, HCF).
- L. Bertinetto, J. Valmadre, S. Golodetz, O. Miksik, and P. Torr. Staple: Complementary learners for real-time tracking. In CVPR, 2016.
- C. Ma, J.-B. Huang, X. Yang, and M.-H. Yang. Hierarchical convolutional features for visual tracking. In ICCV, 2015.