Attention-Aware Multiple Granularities Network for Player Re-Identification

Qi An, Kuilong Cui, Rongshuai Liu, Chuanming Wang, Mengshi Qi, Huadong Ma

Abstract: With the development of deep learning technologies, the performance of person re-identification (ReID) has been greatly improved. However, as a subdomain of person ReID, the research for player ReID is important for the sports field yet lacks sufficient effort so far. Player ReID aims to retrieve a specified player from a gallery of players' images captured by different cameras at various time steps. Compared with the traditional person ReID, player ReID suffers from various difficult problems, e.g., the high similarity of players' appearance, limited-scale datasets, variable and low image resolution, and severe occlusion. To solve such a challenging task, we propose a method named as Attention-Aware Multiple Granularities Network (A$^2$MGN), which consists of multiple branches to capture discriminative features of players from different granularities. Through the criterion of triplet loss and cross-entropy loss, the model can localize different parts of the player and make a comprehensive comparison between each pair of images. Extensive experiments demonstrate the effectiveness and superiority of our method, and our team (MM2022-bupt) achieves the top-3 in the challenge of ACM MMSports 2022 with mAP of 0.96, Rank-1 of 0.99, and Rank-5 of 1.00.