LAUSR

Abstract In many public security applications such as anomaly detection, it is important to re-identify a group of pedestrians by other surveillance cameras, which ascribes to the group retrieval problem. Most previous studies focus on single-person re-identification (re-id) and ignore the correlations among group members, and they lack a large and comprehensive group retrieval benchmark to associate these two tasks. To address this issue, this paper focuses on solving the group retrieval problem and uses it to improve re-id. First, the paper build a comprehensive benchmark for both group retrieval and the group-aided re-id task by proposing a novel pedestrian group retrieval dataset named “SYSU-Group” and a corresponding group-associated re-id dataset named “Group-reID”, which introduces realistic challenges such as variations of pose, viewpoint, illumination, and intra-group layout. The paper then proposes the Siamese Verification-Identification-based Group Retrieval (SVIGR) method, which combines verification and identification modules in a Siamese network to extract robust person features and follows the principle of minimum distance matching to realize group retrieval. Finally, a group-guided re-id method named group retrieval correlation (GRC) is proposed to improve re-id with additional group information. Experimental results on three various group retrieval benchmarks demonstrate the superiority and effectiveness of our method.