A framework for recognizing multi-agent action from visual evidence
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
View-Invariant Representation and Recognition of Actions
International Journal of Computer Vision
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Computer Vision and Image Understanding - Special isssue on video retrieval and summarization
Analysis of Player Actions in Selected Hockey Game Situations
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
Monocular 3-D Tracking of the Golf Swing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Detection of slow-motion replay segments in sports video for highlights generation
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
Trajectory based assessment of coordinated human activity
ICVS'03 Proceedings of the 3rd international conference on Computer vision systems
Analysis of multi-agent activity using petri nets
Pattern Recognition
Histograms of optical flow for efficient representation of body motion
Pattern Recognition Letters
Research opportunities in contextualized fusion systems. the harbor surveillance case
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Group behavior recognition in context-aware systems
IWANN'11 Proceedings of the 11th international conference on Artificial neural networks conference on Advances in computational intelligence - Volume Part II
Tactic analysis based on real-world ball trajectory in soccer video
Pattern Recognition
Team activity recognition in sports
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VII
Video visualization for snooker skill training
EuroVis'10 Proceedings of the 12th Eurographics / IEEE - VGTC conference on Visualization
International Journal of Computer Vision
Assessing team strategy using spatiotemporal data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Take your eyes off the ball: Improving ball-tracking by focusing on team play
Computer Vision and Image Understanding
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This paper proposes a novel, trajectory-based approach to the automatic recognition of complex multi-player behavior in a basketball game. First, a probabilistic play model is applied to the player-trajectory data in order to segment the play into game phases (offense, defense, time out). In this way, both the temporal boundaries of the observed activity and its broader context are obtained. Next, the team's activity is analyzed in more detail by detecting the key elements of basketball play. Following basketball theory, these key elements (starting formation, screen, and move) are the building blocks of basketball play, and therefore their temporal order is used to produce a semantic description of the observed activity. Finally, the activity is recognized by comparing its semantic description with the descriptions of manually defined templates, stored in a database. The effectiveness and robustness of the proposed approach is demonstrated on two championship games and 71 examples of three types of basketball offense.