Jersey Number Recognition using Keyframe Identification from
  Low-Resolution Broadcast Videos
Bavesh Balaji Jerrin Bright Harish Prakash Yuhao Chen David A Clausi John Zelek
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.