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