  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.
