Recognizing player jersey number in sports match video streams is a challenging computer vision task. The human pose and view-point variations displayed in frames lead to many difficulties in recognizing the digits on jerseys. These challenges are addressed here using an approach that exploits human body part cues with a Region-based Convolutional Neural Network (R-CNN) variant for digit level localization and classification. The paper first adopts the Region Proposal Network (RPN) to perform anchor classification and bounding-box regression over three classes: background, person and digit. The person and digit proposals are geometrically related and fed to a network classifier. Subsequently, it introduces a human body key-point prediction branch and a pose-guided regressor to get better bounding-box offsets for generating digit proposals. A novel dataset of soccer-match video frames with corresponding multi-digit class labels, player and jersey number bounding boxes, and single digit segmentation masks is collected. Our framework outperforms all existing models on jersey number recognition task. This work will be essential to the automation of player identification across multiple sports, and releasing the dataset will ease future research on sports video analysis.
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