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• W2170245723 abstract "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." @default.
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• W2170245723 date "2013-06-01" @default.
• W2170245723 modified "2023-10-18" @default.
• W2170245723 title "Representing and Discovering Adversarial Team Behaviors Using Player Roles" @default.
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• W2170245723 doi "https://doi.org/10.1109/cvpr.2013.349" @default.
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