Papers › High Performance Visual Tracking With Siamese Region Proposal Network
High Performance Visual Tracking With Siamese Region Proposal Network
Bo Li, Junjie Yan, Wei Wu, Zheng Zhu, Xiaolin Hu
Visual object tracking has been a fundamental topic in recent years and many deep learning based trackers have achieved state-of-the-art performance on multiple benchmarks. However, most of these trackers can hardly get top performance with real-time speed. In this paper, we propose the Siamese region proposal network (Siamese-RPN) which is end-to-end trained off-line with large-scale image pairs. Specifically, it consists of Siamese subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch. In the inference phase, the proposed framework is formulated as a local one-shot detection task. We can pre-compute the template branch of the Siamese subnetwork and formulate the correlation layers as trivial convolution layers to perform online tracking. Benefit from the proposal refinement, traditional multi-scale test and online fine-tuning can be discarded. The Siamese-RPN runs at 160 FPS while achieving leading performance in VOT2015, VOT2016 and VOT2017 real-time challenges.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Video Object Tracking | NT-VOT211 | SiamRPN | AUC | 33.92 | #25 of 43 | Archive leaderboard | report |
| Video Object Tracking | NT-VOT211 | SiamRPN | Precision | 44.04 | #25 of 43 | Archive leaderboard | report |
| Visual Object Tracking | VOT2017/18 | SiamRPN | Expected Average Overlap (EAO) | 0.383 | #7 of 15 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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