Representing and Discovering Adversarial Team Behaviors Using Player Roles
Patrick Lucey 1; Alina Bialkowski 1,2; Peter Carr 1; Stuart Morgan 3; Iain Matthews 1; and Yaser Sheikh 4
1 Disney Research Pittsburgh
2 Queensland University of Technology
3 Australian Institute of Sport
4 Carnegie Mellon University
In this paper, we describe 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 a player (or agent) is dependent both on their teammates and adversaries, in addition to the tactics or strategies of the team. We present a method which 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 compact representation can effectively “denoise” erroneous detections as well as enabling temporal analysis, which was previously prohibitive due to the dimensionality of the signal. To evaluate our approach, we used a fully instrumented ﬁeld-hockey pitch with 8 ﬁxed high-deﬁnition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data.