Real-time visual play-break detection in sport events using a context descriptor

The detection of play and break segments in team sports is an essential step towards the automation of live game capture and broadcast. This paper presents a two-stage hierarchical method for play-break detection in non-edited video feeds of sport events. Unlike most existing methods, this algorithm performs action and event recognition on content, and thus does not rely on production cues of broadcast feeds. Moreover, the method does not require player tracking, can be used in real-time, and can be easily adapted to different sports. In the first stage, bag-of-words event detectors are trained to recognize key events such as line changes, face-offs and preliminary play-breaks. In the second stage, the output of the detectors along with a novel feature based on spatio-temporal interest points are used to create a context descriptor for the final decision. Experiments demonstrate the efficiency of the proposed method on real hockey game footage, achieving 90% accuracy.