作者：Jeongseok Hyun，Myunggu Kang，Dongyoon Wee，Dit-Yan Yeung
作者单位：香港科技大学
发布时间：2022
发布期刊/会议：Arxiv
出版商：
论文全称：Detection Recovery in Online Multi-Object Tracking with Sparse Graph Tracker
论文地址：
Detection Recovery in Online Multi-Object Tracking with Sparse...
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In existing joint detection and tracking methods, pair-wise relational features are used to match previous tracklets to current detections. However , the features may not be discriminative enough for a tracker to identify a target from a large number of detections. Selecting only high-scored detections for tracking may lead to missed detections whose confidence score is low. Consequently, in the online setting, this results in disconnections of tracklets which cannot be recovered. In this regard, we present Sparse Graph Tracker (SGT), a novel online graph tracker using higher-order relational features which are more discriminative by aggregating the features of neighboring detections and their relations. SGT converts video data into a graph where detections, their connections, and the relational features of two connected nodes are represented by nodes, edges, and edge features, respectively. The strong edge features allow SGT to track targets with tracking can didates selected by top-K scored detections with large K. As a result, even low-scored detections can be tracked, and the missed detections are also recovered. The robustness of K value is shown through the extensive experiments. In the MOT16/17/20 and HiEve Challenge, SGT outperforms the state-of-the-art trackers with real-time inference speed. Especially, a large improvement in MOTA is shown in the MOT20 and HiEve Challenge.