  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.
