This is the research code for the paper:
Chao Ma, Jia-Bin Huang, Xiaokang Yang and Ming-Hsuan Yang, "Hierarchical Convolutional Features for Visual Tracking", ICCV 2015
The correlation filters with convolutional features (CF2) is a state-of-the-art tracker that exploits rich feature hierarchy from deep convolutional neural networks for visual tracking. For more details, please visit our Project page.
If you find the code and dataset useful in your research, please consider citing:
@inproceedings{Ma-ICCV-2015,
title={Hierarchical Convolutional Features for Visual Tracking},
Author = {Ma, Chao and Huang, Jia-Bin and Yang, Xiaokang and Yang, Ming-Hsuan},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision},
pages={},
Year = {2015}
}
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Feedbacks and comments are welcome! Feel free to contact us via chaoma99@gmail.com or jbhuang1@illinois.edu.
Enjoy!
One-pass evaluation (OPE) on the 50 tracking sequences in Wu et al. CVPR 2013
Spatial robustness evaluation (SRE) on the 50 tracking sequences in Wu et al. CVPR 2013
Temporal robustness evaluation (TRE) on the 50 tracking sequences in Wu et al. CVPR 2013
One-pass evaluation (OPE) on the 100 tracking sequences in Wu et al. PAMI 2015
Spatial robustness evaluation (SRE) on the 100 tracking sequences in Wu et al. PAMI 2015
Temporal robustness evaluation (TRE) on the 100 tracking sequences in Wu et al. PAMI 2015
