论文标题
体育重新ID：改善在团队运动的广播视频中重新识别玩家
Sports Re-ID: Improving Re-Identification Of Players In Broadcast Videos Of Team Sports
论文作者
论文摘要
这项工作重点是在团队运动的广播视频中重新识别。具体来说，我们专注于在比赛的任何给定时刻从不同的相机观点捕获的图像中识别相同的播放器。这项任务与人重新ID的传统应用不同。首先，来自同一团队的球员穿着高度相似的衣服，从而使他们很难分开。其次，每个身份只有几个样本，这使得训练重新ID系统变得更加困难。第三，图像的分辨率通常很低，并且有很大差异。这加上大量的阻塞和玩家的快速运动大大增加了Re-ID的挑战。在本文中，我们提出了一个简单但有效的层次数据采样程序和一种质心损失函数，当一起使用时，将平均平均精度（MAP）提高7-11.5，而Rank -1（r1）则将8.8-14.9提高，而没有任何网络或超级参数的变化。我们的数据采样程序提高了训练和测试分布的相似性，从而有助于对嵌入式质心（或特征向量）进行更好的估计。令人惊讶的是，我们的研究表明，在存在严重有限的数据的情况下，与我们的应用一样，基于欧几里得距离的简单质心损耗函数极大地胜过流行的三重态 - 触觉损失函数。我们对卷积网络和视觉变压器都显示出可比的改进。我们的方法是Soccernet重新识别挑战2022排行榜（测试）中排名最高的方法之一，地图为86.0，R1为81.5。在隔离的挑战赛中，我们获得了84.9的地图，R1为80.1。关于与运动相关应用的RE-ID的研究非常有限，我们的工作介绍了文献中关于此的第一批讨论之一。
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.
