CF2

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Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)

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Hierarchical Convolutional Features for Visual Tracking (ICCV 2015)

Introduction

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
- PDF - Supplementary material - Project page - Result visualization

Compared to the original implemetation, we have improved the code to achieve better results: 1. We added the scale estimation module 2. We adjust the layer weights according to our extension work published on TPAMI

To exactly reproduce the results reported in our ICCV 2015 paper, please check the early committed version (4b895b5)

Citation

If you find the code and dataset useful in your research, please consider citing:

@article{Ma-HCFTstar-2017,
    title={Robust Visual Tracking via Hierarchical Convolutional Features},
    Author = {Ma, Chao and Huang, Jia-Bin and Yang, Xiaokang and Yang, Ming-Hsuan},
    journal = {IEEE Transcations on Pattern Analysis and Machine Intelligence},
    pages={},
    Year = {2018}
}

@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} }

Contents

| Folder | description | | ---|---|

Feedbacks and comments are welcome! Feel free to contact us via [email protected] or [email protected].

Enjoy!

Results on visual tracking benchmark

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

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