Reasoning about Dynamic Scenes Using Autonomous Agents

Abstract

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.