期刊
IEEE TRANSACTIONS ON IMAGE PROCESSING
卷 29, 期 -, 页码 7957-7969出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIP.2020.3009034
关键词
Multi-object tracking; sports video analysis; siamese network
资金
- National Key Research and Development Plan of China [2018AAA0102301]
- Research Program of State Key Laboratory of Software Development Environment [SKLSDE2019ZX-03]
- Fundamental Research Funds for the Central Universities
Tracking multiple athletes in sports videos is a very challenging Multi-Object Tracking (MOT) task, as athletes generally share high similarity in appearance with large deformations. In this paper, unlike the existing hand-crafted solutions, we propose a novel and effective approach to this issue, which hierarchically associates detections of the same identity through discriminative and robust deep features. First, in detection association, we make use of athlete appearances and poses instead of traditional position cues to generate short tracklets for better initialization. Second, in tracklet association, a new deep architecture, namely Siamese Tracklet Affinity Networks (STAN), is presented, which is able to bi-directionally simulate the unseen dynamics of actions, comprehensively models the long-term action dependences, and sequentially estimates their affinity. Such hierarchical association is finally solved as a minimum-cost network flow problem. We extensively evaluate the proposed approach on the APIDIS, NCAA Basketball and VolleyTrack (newly collected) databases, and the experimental results show its advantages.
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