IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910827148214272 |
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| author | Liu, Xiaohong Zhao, Xulong Liu, Gang Wu, Zili Wang, Tao Meng, Lei Wang, Yuhan |
| author_facet | Liu, Xiaohong Zhao, Xulong Liu, Gang Wu, Zili Wang, Tao Meng, Lei Wang, Yuhan |
| contents | 3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object throughout its entire tracking process. However, objects may change their motion patterns due to variations in the surrounding environment. In this paper, we introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects, overcoming the limitation of single-model tracking in existing approaches. In addition, we incorporate a Damping Window mechanism into the trajectory lifecycle management, leveraging the continuous association status of trajectories to control their creation and termination, reducing the occurrence of overlooked low-confidence true targets. Furthermore, we propose the Distance-Based Score Enhancement module, which enhances the differentiation between false positives and true positives by adjusting detection scores, thereby improving the effectiveness of the Score Filter. On the NuScenes Val dataset, IMM-MOT outperforms most other single-modal models using 3D point clouds, achieving an AMOTA of 73.8%. Our project is available at https://github.com/Ap01lo/IMM-MOT. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_09672 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter Liu, Xiaohong Zhao, Xulong Liu, Gang Wu, Zili Wang, Tao Meng, Lei Wang, Yuhan Computer Vision and Pattern Recognition Robotics 65D19, 68T40 3D Multi-Object Tracking (MOT) provides the trajectories of surrounding objects, assisting robots or vehicles in smarter path planning and obstacle avoidance. Existing 3D MOT methods based on the Tracking-by-Detection framework typically use a single motion model to track an object throughout its entire tracking process. However, objects may change their motion patterns due to variations in the surrounding environment. In this paper, we introduce the Interacting Multiple Model filter in IMM-MOT, which accurately fits the complex motion patterns of individual objects, overcoming the limitation of single-model tracking in existing approaches. In addition, we incorporate a Damping Window mechanism into the trajectory lifecycle management, leveraging the continuous association status of trajectories to control their creation and termination, reducing the occurrence of overlooked low-confidence true targets. Furthermore, we propose the Distance-Based Score Enhancement module, which enhances the differentiation between false positives and true positives by adjusting detection scores, thereby improving the effectiveness of the Score Filter. On the NuScenes Val dataset, IMM-MOT outperforms most other single-modal models using 3D point clouds, achieving an AMOTA of 73.8%. Our project is available at https://github.com/Ap01lo/IMM-MOT. |
| title | IMM-MOT: A Novel 3D Multi-object Tracking Framework with Interacting Multiple Model Filter |
| topic | Computer Vision and Pattern Recognition Robotics 65D19, 68T40 |
| url | https://arxiv.org/abs/2502.09672 |