1. Automated Tackle Injury Risk Assessment in Contact-Based Sports -- A Rugby Union Example
- Author
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Martin, Zubair, Patel, Amir, and Hendricks, Sharief
- Subjects
Computer Science - Computer Vision and Pattern Recognition - Abstract
Video analysis in tackle-collision based sports is highly subjective and exposed to bias, which is inherent in human observation, especially under time constraints. This limitation of match analysis in tackle-collision based sports can be seen as an opportunity for computer vision applications. Objectively tracking, detecting and recognising an athlete's movements and actions during match play from a distance using video, along with our improved understanding of injury aetiology and skill execution will enhance our understanding how injury occurs, assist match day injury management, reduce referee subjectivity. In this paper, we present a system of objectively evaluating in-game tackle risk in rugby union matches. First, a ball detection model is trained using the You Only Look Once (YOLO) framework, these detections are then tracked by a Kalman Filter (KF). Following this, a separate YOLO model is used to detect persons/players within a tackle segment and then the ball-carrier and tackler are identified. Subsequently, we utilize OpenPose to determine the pose of ball-carrier and tackle, the relative pose of these is then used to evaluate the risk of the tackle. We tested the system on a diverse collection of rugby tackles and achieved an evaluation accuracy of 62.50%. These results will enable referees in tackle-contact based sports to make more subjective decisions, ultimately making these sports safer., Comment: The paper has five figures and is eight pages long (excluding references). The paper has been entered into the 7th IEEE International Workshop on Computer Vision in Sports (CVSports)
- Published
- 2021