Fundamentals of speech recognition
Fundamentals of speech recognition
Visual surveillance in a dynamic and uncertain world
Artificial Intelligence - Special volume on computer vision
A synthetic agent system for Bayesian modeling of human interactions
Proceedings of the third annual conference on Autonomous Agents
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
A general probabilistic framework for clustering individuals and objects
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Agent Orientated Annotation in Model Based Visual Surveillance
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Approximate inference for first-order probabilistic languages
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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The scene interpretation system proposed below integrates computer vision and artificial intelligence techniques to combine the information generated by multiple cameras on typical secure sites. A multi-agent architecture is proposed as the backbone of the system within which the agents control the different components of the system and incrementally build a model of the scene by merging the information gathered over time and between cameras. The choice of a distributed artificial intelligence architecture is justified by the need for scalable designs capable of co-operating to infer an optimal interpretation of the scene. Decentralizing intelligence means creating more robust and reliable sources of interpretation, but also allows easy maintenance and updating of the system. The scene model is learned using Hidden Markov models which capture the range of possible scene behaviours. The employment of such probabilistic interpretation techniques is justified by the very nature of surveillance data, which is typically incomplete, uncertain and asynchronous.