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| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.00236 |
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| _version_ | 1866909186244214784 |
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| author | Jing, Longlong Yu, Ruichi Chen, Xu Zhao, Zhengli Sheng, Shiwei Graber, Colin Chen, Qi Li, Qinru Wu, Shangxuan Deng, Han Lee, Sangjin Sweeney, Chris He, Qiurui Hung, Wei-Chih He, Tong Zhou, Xingyi Moussavi, Farshid Guo, Zijian Zhou, Yin Tan, Mingxing Yang, Weilong Li, Congcong |
| author_facet | Jing, Longlong Yu, Ruichi Chen, Xu Zhao, Zhengli Sheng, Shiwei Graber, Colin Chen, Qi Li, Qinru Wu, Shangxuan Deng, Han Lee, Sangjin Sweeney, Chris He, Qiurui Hung, Wei-Chih He, Tong Zhou, Xingyi Moussavi, Farshid Guo, Zijian Zhou, Yin Tan, Mingxing Yang, Weilong Li, Congcong |
| contents | Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_00236 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STT: Stateful Tracking with Transformers for Autonomous Driving Jing, Longlong Yu, Ruichi Chen, Xu Zhao, Zhengli Sheng, Shiwei Graber, Colin Chen, Qi Li, Qinru Wu, Shangxuan Deng, Han Lee, Sangjin Sweeney, Chris He, Qiurui Hung, Wei-Chih He, Tong Zhou, Xingyi Moussavi, Farshid Guo, Zijian Zhou, Yin Tan, Mingxing Yang, Weilong Li, Congcong Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning Tracking objects in three-dimensional space is critical for autonomous driving. To ensure safety while driving, the tracker must be able to reliably track objects across frames and accurately estimate their states such as velocity and acceleration in the present. Existing works frequently focus on the association task while either neglecting the model performance on state estimation or deploying complex heuristics to predict the states. In this paper, we propose STT, a Stateful Tracking model built with Transformers, that can consistently track objects in the scenes while also predicting their states accurately. STT consumes rich appearance, geometry, and motion signals through long term history of detections and is jointly optimized for both data association and state estimation tasks. Since the standard tracking metrics like MOTA and MOTP do not capture the combined performance of the two tasks in the wider spectrum of object states, we extend them with new metrics called S-MOTA and MOTPS that address this limitation. STT achieves competitive real-time performance on the Waymo Open Dataset. |
| title | STT: Stateful Tracking with Transformers for Autonomous Driving |
| topic | Robotics Artificial Intelligence Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2405.00236 |