Representing and discovering adversarial team behaviors using player roles

Lucey, Patrick J., Bialkowski, Alina, Carr, Peter, Morgan, Stuart, Matthews, Iain, & Sheikh, Yaser (2013) Representing and discovering adversarial team behaviors using player roles. In IEEE International Conference on Computer Vision and Pattern Recognition, IEEE, Portland, Oregon, USA, pp. 2706-2713.

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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 enabe 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.

Impact and interest:

4 citations in Scopus
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6 citations in Web of Science®

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ID Code: 63233
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1109/CVPR.2013.349
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Computer Vision (080104)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Pattern Recognition and Data Mining (080109)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Deposited On: 10 Oct 2013 03:48
Last Modified: 11 Oct 2013 00:14

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