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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.18006 |
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| _version_ | 1866917283970940928 |
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| author | Bakht, Ahsan Baidar Alansari, Mohamad Din, Muhayy Ud Naseer, Muzammal Javed, Sajid Hussain, Irfan Matas, Jiri Mahmood, Arif |
| author_facet | Bakht, Ahsan Baidar Alansari, Mohamad Din, Muhayy Ud Naseer, Muzammal Javed, Sajid Hussain, Irfan Matas, Jiri Mahmood, Arif |
| contents | Underwater Object Tracking (UOT) is crucial for efficient marine robotics, large scale ecological monitoring, and ocean exploration; however, progress has been hindered by the scarcity of large, multimodal, and diverse datasets. Existing benchmarks remain small and RGB only, limiting robustness under severe color distortion, turbidity, and low visibility conditions. We introduce MUOT_3M, the first pseudo multimodal UOT benchmark comprising 3 million frames from 3,030 videos (27.8h) annotated with 32 tracking attributes, 677 fine grained classes, and synchronized RGB, estimated enhanced RGB, estimated depth, and language modalities validated by a marine biologist. Building upon MUOT_3M, we propose MUTrack, a SAM-based multimodal to unimodal tracker featuring visual geometric alignment, vision language fusion, and four level knowledge distillation that transfers multimodal knowledge into a unimodal student model. Extensive evaluations across five UOT benchmarks demonstrate that MUTrack achieves up to 8.40% higher AUC and 7.80% higher precision than the strongest SOTA baselines while running at 24 FPS. MUOT_3M and MUTrack establish a new foundation for scalable, multimodally trained yet practically deployable underwater tracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_18006 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | MUOT_3M: A 3 Million Frame Multimodal Underwater Benchmark and the MUTrack Tracking Method Bakht, Ahsan Baidar Alansari, Mohamad Din, Muhayy Ud Naseer, Muzammal Javed, Sajid Hussain, Irfan Matas, Jiri Mahmood, Arif Computer Vision and Pattern Recognition Underwater Object Tracking (UOT) is crucial for efficient marine robotics, large scale ecological monitoring, and ocean exploration; however, progress has been hindered by the scarcity of large, multimodal, and diverse datasets. Existing benchmarks remain small and RGB only, limiting robustness under severe color distortion, turbidity, and low visibility conditions. We introduce MUOT_3M, the first pseudo multimodal UOT benchmark comprising 3 million frames from 3,030 videos (27.8h) annotated with 32 tracking attributes, 677 fine grained classes, and synchronized RGB, estimated enhanced RGB, estimated depth, and language modalities validated by a marine biologist. Building upon MUOT_3M, we propose MUTrack, a SAM-based multimodal to unimodal tracker featuring visual geometric alignment, vision language fusion, and four level knowledge distillation that transfers multimodal knowledge into a unimodal student model. Extensive evaluations across five UOT benchmarks demonstrate that MUTrack achieves up to 8.40% higher AUC and 7.80% higher precision than the strongest SOTA baselines while running at 24 FPS. MUOT_3M and MUTrack establish a new foundation for scalable, multimodally trained yet practically deployable underwater tracking. |
| title | MUOT_3M: A 3 Million Frame Multimodal Underwater Benchmark and the MUTrack Tracking Method |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2602.18006 |