Siamese-DETR for Generic Multi-Object Tracking

• URL: http://arxiv.org/abs/2310.17875v2
• Date: Fri, 14 Jun 2024 18:00:34 GMT
• Title: Siamese-DETR for Generic Multi-Object Tracking
• Authors: Qiankun Liu, Yichen Li, Yuqi Jiang, Ying Fu,
• Abstract summary: Traditional Multi-Object Tracking (MOT) is limited to tracking objects belonging to the pre-defined closed-set categories. Siamese-DETR is proposed to track objects beyond pre-defined categories with the given text prompt and template image. Siamese-DETR surpasses existing MOT methods on GMOT-40 dataset by a large margin.
• Score: 16.853363984562602
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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