Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
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
Maintaining knowledge about temporal intervals
Communications of the ACM
Robust recognition of physical team behaviors using spatio-temporal models
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Detecting social interactions of the elderly in a nursing home environment
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
A trajectory-based analysis of coordinated team activity in a basketball game
Computer Vision and Image Understanding
A study of detecting social interaction with sensors in a nursing home environment
ICCV'05 Proceedings of the 2005 international conference on Computer Vision in Human-Computer Interaction
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Most approaches to detection and classification of human activity deal with observing individual persons. However, people often tend to organize into groups to achieve certain goals, and human activity is sometimes more readily defined and observed in the context of whole group, where the activity is coordinated among its members. An excellent example of this are team sports, which can provide valuable test ground for development of methods for analysis of coordinated group activity. We used basketball play in this work and developed a probabilistic model of a team play, which is based on the detection of key events in the team behavior. The model is based on expert coach knowledge and has been used to assess the team performance in three different types of basketball offense, based on trajectories of all players, obtained by whole-body tracker. Results show that our high-level behaviour model may be used both for activity recognition and performance evaluation in certain basketball activities.