Siamese-DETR for Generic Multi-Object Tracking
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866915092752236544 |
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| author | Liu, Qiankun Li, Yichen Jiang, Yuqi Fu, Ying |
| author_facet | Liu, Qiankun Li, Yichen Jiang, Yuqi Fu, Ying |
| contents | The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image. However, the expensive well pre-trained (vision-)language model and fine-grained category annotations are required to train OVMOT models. In this paper, we focus on GMOT and propose a simple but effective method, Siamese-DETR, for GMOT. Only the commonly used detection datasets (e.g., COCO) are required for training. Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants. Specifically: 1) The multi-scale object queries are designed based on the given template image, which are effective for detecting different scales of objects with the same category as the template image; 2) A dynamic matching training strategy is introduced to train Siamese-DETR on commonly used detection datasets, which takes full advantage of provided annotations; 3) The online tracking pipeline is simplified through a tracking-by-query manner by incorporating the tracked boxes in previous frame as additional query boxes. The complex data association is replaced with the much simpler Non-Maximum Suppression (NMS). Extensive experimental results show that Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin. Codes are avaliable at \url{https://github.com/yumu-173/Siamese-DETR}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_17875 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Siamese-DETR for Generic Multi-Object Tracking Liu, Qiankun Li, Yichen Jiang, Yuqi Fu, Ying Computer Vision and Pattern Recognition The ability to detect and track the dynamic objects in different scenes is fundamental to real-world applications, e.g., autonomous driving and robot navigation. However, traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Recently, Open-Vocabulary MOT (OVMOT) and Generic MOT (GMOT) are proposed to track interested objects beyond pre-defined categories with the given text prompt and template image. However, the expensive well pre-trained (vision-)language model and fine-grained category annotations are required to train OVMOT models. In this paper, we focus on GMOT and propose a simple but effective method, Siamese-DETR, for GMOT. Only the commonly used detection datasets (e.g., COCO) are required for training. Different from existing GMOT methods, which train a Single Object Tracking (SOT) based detector to detect interested objects and then apply a data association based MOT tracker to get the trajectories, we leverage the inherent object queries in DETR variants. Specifically: 1) The multi-scale object queries are designed based on the given template image, which are effective for detecting different scales of objects with the same category as the template image; 2) A dynamic matching training strategy is introduced to train Siamese-DETR on commonly used detection datasets, which takes full advantage of provided annotations; 3) The online tracking pipeline is simplified through a tracking-by-query manner by incorporating the tracked boxes in previous frame as additional query boxes. The complex data association is replaced with the much simpler Non-Maximum Suppression (NMS). Extensive experimental results show that Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin. Codes are avaliable at \url{https://github.com/yumu-173/Siamese-DETR}. |
| title | Siamese-DETR for Generic Multi-Object Tracking |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.17875 |