Motion Fields to Predict Play Evolution in Dynamic Sport Scenes

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Title: Motion Fields to Predict Play Evolution in Dynamic Sport Scenes
Author: Kim, Kihwan ; Grundmann, Matthias ; Shamir, Ariel ; Matthews, Iain ; Hodgins, Jessica ; Essa, Irfan
Abstract: Videos of multi-player team sports provide a challenging domain for dynamic scene analysis. Player actions and interactions are complex as they are driven by many factors, such as the short-term goals of the individual player, the overall team strategy, the rules of the sport, and the current context of the game. We show that constrained multi-agent events can be analyzed and even predicted from video. Such analysis requires estimating the global movements of all players in the scene at any time, and is needed for modeling and predicting how the multi-agent play evolves over time on the field. To this end, we propose a novel approach to detect the locations of where the play evolution will proceed, e.g. where interesting events will occur, by tracking player positions and movements over time. We start by extracting the ground level sparse movement of players in each time-step, and then generate a dense motion field. Using this field we detect locations where the motion converges, implying positions towards which the play is evolving. We evaluate our approach by analyzing videos of a variety of complex soccer plays.
Description: ©2010 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Presented at the 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June 2010, San Francisco, CA. DOI: 10.1109/CVPR.2010.5540128
Type: Post-print
ISSN: 1063-6919
Citation: Kim, K., Grundmann, M., Shamir, A., Matthews, I., Hodgins, J., & Essa, I. (2010). "Motion Fields to Predict Play Evolution in Dynamic Sport Scenes". Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2010), 13-18 June 2010, 840-847.
Date: 2010-06
Contributor: Georgia Institute of Technology. Center for Robotics and Intelligent Machines
Georgia Institute of Technology. College of Computing
Interdisciplinary Center (Herzliya, Israel)
Disney Research
Publisher: Georgia Institute of Technology
Institute of Electrical and Electronics Engineers
Subject: Motion field
Multi-agent systems
Team sports

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