Automatic player identification is an essential and complex task in sports video analysis. Different strategies have been devised over the years, but identification based on jersey numbers is one of the most common approaches given its versatility and relative simplicity. However, automatic detection of jersey numbers is still challenging due to changing camera angles, low video resolution, small object size in wide-range shots and transient changes in the player's posture and movement. In this paper we present a novel approach for jersey number identification in a small, highly imbalanced dataset from the Seattle Seahawks practice videos. Our results indicate that simple models can achieve an acceptable performance on the jersey number detection task and that synthetic data can improve the performance dramatically (accuracy increase of ~9% overall, ~18% on low frequency numbers) making our approach achieve state of the art results.

本文提出了一种新的方法来解决在Seattle Seahawks体育练习视频中，基于球衣号码的自动识别仍然具有挑战性的问题，我们的结果表明，使用合成数据可以显著地提高球衣号码检测任务的性能（总体精度可提高~9％，低频数字可提高~18％），从而使我们的方法达到业界领先水平。

合成数据识别足球运动员球衣号码