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This work focuses on player re-identification in broadcast videos of teamsports. Specifically, we focus on identifying the same player in imagescaptured from different camera viewpoints during any given moment of a match.This task differs from traditional applications of person re-id in a fewimportant ways. Firstly, players from the same team wear highly similarclothes, thereby making it harder to tell them apart. Secondly, there are onlya few number of samples for each identity, which makes it harder to train are-id system. Thirdly, the resolutions of the images are often quite low andvary a lot. This combined with heavy occlusions and fast movements of playersgreatly increase the challenges for re-id. In this paper, we propose a simplebut effective hierarchical data sampling procedure and a centroid loss functionthat, when used together, increase the mean average precision (mAP) by 7 - 11.5and the rank-1 (R1) by 8.8 - 14.9 without any change in the network orhyper-parameters used. Our data sampling procedure improves the similarity ofthe training and test distributions, and thereby aids in creating betterestimates of the centroids of the embeddings (or feature vectors).Surprisingly, our study shows that in the presence of severely limited data, asis the case for our application, a simple centroid loss function based oneuclidean distances significantly outperforms the popular triplet-centroid lossfunction. We show comparable improvements for both convolutional networks andvision transformers. Our approach is among the top ranked methods in theSoccerNet Re-Identification Challenge 2022 leaderboard (test-split) with a mAPof 86.0 and a R1 of 81.5. On the sequestered challenge split, we achieve an mAPof 84.9 and a R1 of 80.1. Research on re-id for sports-related applications isvery limited and our work presents one of the first discussions in theliterature on this.

Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports