Biomedical Engineering, Stevens Institute of Technology, Hoboken, 07030, NJ, US.
Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, 35487, AL, US.
Sci Rep. 2023 Jun 20;13(1):10036. doi: 10.1038/s41598-023-36657-5.
American football games attract significant worldwide attention every year. Identifying players from videos in each play is also essential for the indexing of player participation. Processing football game video presents great challenges such as crowded settings, distorted objects, and imbalanced data for identifying players, especially jersey numbers. In this work, we propose a deep learning-based player tracking system to automatically track players and index their participation per play in American football games. It is a two-stage network design to highlight areas of interest and identify jersey number information with high accuracy. First, we utilize an object detection network, a detection transformer, to tackle the player detection problem in a crowded context. Second, we identify players using jersey number recognition with a secondary convolutional neural network, then synchronize it with a game clock subsystem. Finally, the system outputs a complete log in a database for play indexing. We demonstrate the effectiveness and reliability of player tracking system by analyzing the qualitative and quantitative results on football videos. The proposed system shows great potential for implementation in and analysis of football broadcast video.
美式橄榄球比赛每年都吸引着全球的广泛关注。在每场比赛的视频中识别球员对于球员参与情况的索引也至关重要。处理足球比赛视频带来了巨大的挑战，例如拥挤的场景、变形的物体和用于识别球员（尤其是球衣号码）的不平衡数据。在这项工作中，我们提出了一个基于深度学习的球员跟踪系统，以自动跟踪球员并为每场比赛的参与情况进行索引。这是一个两阶段的网络设计，旨在突出感兴趣的区域，并以高精度识别球衣号码信息。首先，我们利用目标检测网络（检测变形金刚）来解决拥挤场景中的球员检测问题。其次，我们使用二级卷积神经网络进行球衣号码识别来识别球员，然后与游戏时钟子系统同步。最后，系统将完整的日志输出到数据库中，以便进行比赛索引。我们通过对足球视频的定性和定量分析结果证明了球员跟踪系统的有效性和可靠性。该系统在足球广播视频的分析和应用中具有很大的潜力。
