Papers › RPT: Learning Point Set Representation for Siamese Visual Tracking
RPT: Learning Point Set Representation for Siamese Visual Tracking
Ziang Ma, Linyuan Wang, HaiTao Zhang, Wei Lu, Jun Yin
While remarkable progress has been made in robust visual tracking, accurate target state estimation still remains a highly challenging problem. In this paper, we argue that this issue is closely related to the prevalent bounding box representation, which provides only a coarse spatial extent of object. Thus an effcient visual tracking framework is proposed to accurately estimate the target state with a finer representation as a set of representative points. The point set is trained to indicate the semantically and geometrically significant positions of target region, enabling more fine-grained localization and modeling of object appearance. We further propose a multi-level aggregation strategy to obtain detailed structure information by fusing hierarchical convolution layers. Extensive experiments on several challenging benchmarks including OTB2015, VOT2018, VOT2019 and GOT-10k demonstrate that our method achieves new state-of-the-art performance while running at over 20 FPS.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Semi-Supervised Video Object Segmentation | VOT2020 | RPT | EAO | 0.530 | #16 of 20 | Archive leaderboard | report |
| Semi-Supervised Video Object Segmentation | VOT2020 | RPT | EAO (real-time) | 0.290 | #16 of 20 | Archive leaderboard | report |
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Methods
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