Disney Research


Figure 1. We can identify a player by their name or number (e.g. 1, 2 or 3) or via their formation role (e.g. left wing LW, center forward CF and right wing RW). Given two snapshots of play at time t and t0, using player identity (1, 2, and 3) the two snapshots will look different as the players have swapped positions. However, if we disregard identity and use role (LW, CF, RW), the arrangements are similar which yields a more compact representation and allows for generalization across games.

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 labeled data.

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