Abstract
Video analysis in professional sports is a relatively new assistive tool for coaching. Currently, manual annotation and analysis of video footage is the modus operandi. This is a laborious and time consuming process, which does not afford a cost effective or scalable solution as the demand and uses of video analysis grows. This paper describes a method for automatic annotation and segmentation of video footage of rugby games (one of the sports that pioneered the use of computer vision techniques for game analysis and coaching) into specific events (e.g. a scrum), with the aim to reduce time and cost associated with manual annotation of multiple videos. This is achieved in a data-driven fashion, whereby the models that are used for automatic annotation are trained from video footage. Training data consists of annotated events in a game and corresponding video. We propose a supervised machine learning solution. We use human annotations from a large corpus of international matches to extract video of such events. Dense SIFT (Scale Invariant Feature Transform) features are then extracted for each frame from which a bag-of-words vocabulary is determined. A classifier is then built from labelled data and the features extracted for each corresponding video frame. We present promising results on broadcast video for a international rugby matches annotated by expert video analysts.
