RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913505942175744 |
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| author | Li, Xiaoyu Li, Peidong Zhao, Lijun Liu, Dedong Gao, Jinghan Wu, Xian Wu, Yitao Cui, Dixiao |
| author_facet | Li, Xiaoyu Li, Peidong Zhao, Lijun Liu, Dedong Gao, Jinghan Wu, Xian Wu, Yitao Cui, Dixiao |
| contents | 3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end training approach for multi-camera trackers results in detector-specific models, limiting their versatility. Moreover, current generic trackers overlook the unique features of multi-camera detectors, i.e., the unreliability of motion observations and the feasibility of visual information. To address these challenges, we propose RockTrack, a 3D MOT method for multi-camera detectors. Following the Tracking-By-Detection framework, RockTrack is compatible with various off-the-shelf detectors. RockTrack incorporates a confidence-guided preprocessing module to extract reliable motion and image observations from distinct representation spaces from a single detector. These observations are then fused in an association module that leverages geometric and appearance cues to minimize mismatches. The resulting matches are propagated through a staged estimation process, forming the basis for heuristic noise modeling. Additionally, we introduce a novel appearance similarity metric for explicitly characterizing object affinities in multi-camera settings. RockTrack achieves state-of-the-art performance on the nuScenes vision-only tracking leaderboard with 59.1% AMOTA while demonstrating impressive computational efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_11749 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework Li, Xiaoyu Li, Peidong Zhao, Lijun Liu, Dedong Gao, Jinghan Wu, Xian Wu, Yitao Cui, Dixiao Computer Vision and Pattern Recognition Robotics 3D Multi-Object Tracking (MOT) obtains significant performance improvements with the rapid advancements in 3D object detection, particularly in cost-effective multi-camera setups. However, the prevalent end-to-end training approach for multi-camera trackers results in detector-specific models, limiting their versatility. Moreover, current generic trackers overlook the unique features of multi-camera detectors, i.e., the unreliability of motion observations and the feasibility of visual information. To address these challenges, we propose RockTrack, a 3D MOT method for multi-camera detectors. Following the Tracking-By-Detection framework, RockTrack is compatible with various off-the-shelf detectors. RockTrack incorporates a confidence-guided preprocessing module to extract reliable motion and image observations from distinct representation spaces from a single detector. These observations are then fused in an association module that leverages geometric and appearance cues to minimize mismatches. The resulting matches are propagated through a staged estimation process, forming the basis for heuristic noise modeling. Additionally, we introduce a novel appearance similarity metric for explicitly characterizing object affinities in multi-camera settings. RockTrack achieves state-of-the-art performance on the nuScenes vision-only tracking leaderboard with 59.1% AMOTA while demonstrating impressive computational efficiency. |
| title | RockTrack: A 3D Robust Multi-Camera-Ken Multi-Object Tracking Framework |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2409.11749 |