Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos

• URL: http://arxiv.org/abs/2309.06285v1
• Date: Tue, 12 Sep 2023 14:43:50 GMT
• Title: Jersey Number Recognition using Keyframe Identification from Low-Resolution Broadcast Videos
• Authors: Bavesh Balaji, Jerrin Bright, Harish Prakash, Yuhao Chen, David A Clausi and John Zelek
• Abstract summary: Player identification is a crucial component in vision-driven soccer analytics, enabling various tasks such as player assessment, in-game analysis, and broadcast evaluations. Previous methods have shown success in image data but struggle with real-world video data, where jersey numbers are not visible in most frames. We propose a robust downstream identification module that extracts frames containing essential high-level information about the jersey number.
• Score: 7.776923607006088
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Player identification is a crucial component in vision-driven soccer analytics, enabling various downstream tasks such as player assessment, in-game analysis, and broadcast production. However, automatically detecting jersey numbers from player tracklets in videos presents challenges due to motion blur, low resolution, distortions, and occlusions. Existing methods, utilizing Spatial Transformer Networks, CNNs, and Vision Transformers, have shown success in image data but struggle with real-world video data, where jersey numbers are not visible in most of the frames. Hence, identifying frames that contain the jersey number is a key sub-problem to tackle. To address these issues, we propose a robust keyframe identification module that extracts frames containing essential high-level information about the jersey number. A spatio-temporal network is then employed to model spatial and temporal context and predict the probabilities of jersey numbers in the video. Additionally, we adopt a multi-task loss function to predict the probability distribution of each digit separately. Extensive evaluations on the SoccerNet dataset demonstrate that incorporating our proposed keyframe identification module results in a significant 37.81% and 37.70% increase in the accuracies of 2 different test sets with domain gaps. These results highlight the effectiveness and importance of our approach in tackling the challenges of automatic jersey number detection in sports videos.

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