Efficient Motion Prompt Learning for Robust Visual Tracking
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912952759615488 |
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| author | Zhao, Jie Chen, Xin Yuan, Yongsheng Felsberg, Michael Wang, Dong Lu, Huchuan |
| author_facet | Zhao, Jie Chen, Xin Yuan, Yongsheng Felsberg, Michael Wang, Dong Lu, Huchuan |
| contents | Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this paper, we propose a lightweight and plug-and-play motion prompt tracking method. It can be easily integrated into existing vision-based trackers to build a joint tracking framework leveraging both motion and vision cues, thereby achieving robust tracking through efficient prompt learning. A motion encoder with three different positional encodings is proposed to encode the long-term motion trajectory into the visual embedding space, while a fusion decoder and an adaptive weight mechanism are designed to dynamically fuse visual and motion features. We integrate our motion module into three different trackers with five models in total. Experiments on seven challenging tracking benchmarks demonstrate that the proposed motion module significantly improves the robustness of vision-based trackers, with minimal training costs and negligible speed sacrifice. Code is available at https://github.com/zj5559/Motion-Prompt-Tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_16321 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Efficient Motion Prompt Learning for Robust Visual Tracking Zhao, Jie Chen, Xin Yuan, Yongsheng Felsberg, Michael Wang, Dong Lu, Huchuan Computer Vision and Pattern Recognition Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this paper, we propose a lightweight and plug-and-play motion prompt tracking method. It can be easily integrated into existing vision-based trackers to build a joint tracking framework leveraging both motion and vision cues, thereby achieving robust tracking through efficient prompt learning. A motion encoder with three different positional encodings is proposed to encode the long-term motion trajectory into the visual embedding space, while a fusion decoder and an adaptive weight mechanism are designed to dynamically fuse visual and motion features. We integrate our motion module into three different trackers with five models in total. Experiments on seven challenging tracking benchmarks demonstrate that the proposed motion module significantly improves the robustness of vision-based trackers, with minimal training costs and negligible speed sacrifice. Code is available at https://github.com/zj5559/Motion-Prompt-Tracking. |
| title | Efficient Motion Prompt Learning for Robust Visual Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.16321 |