Modeling and Recognising Team Strategies, Tactics and Tendencies in Sports
Patrick Lucey (Disney Research Pittsburgh)
Alina Bialkowski (Queensland University of Technology)
Peter Carr (Disney Research Pittsburgh)
Yaser Sheikh (Disney Research Pittsburgh/Carnegie Mellon University)
Iain Matthews (Disney Research Pittsburgh)
We introduce a method to represent and discover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial behavior is heavily structured as the location of an player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. Our method can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that employing a “role-based” representation instead of one based on player identity can best exploit the playing structure. As vision-based systems currently do not provide perfect detection/tracking (e.g. missed or false detections), we show that our approach can “denoise” the signal and assign player roles which enables analysis of team formation and plays in continuous sports occur. Finding a compact representation allows significant temporal analysis to occur, which was previously prohibitive due to the dimensionality of the signal.