A bottom-up approach of fusion of events in surveillance systems

Human observers are able to fuse events and to integrate them in a unifying theory. An incoming stream of events triggers a hypothesis in an associative way. On the other hand, most automated classification systems require a full list of hypotheses with a specified list of events. An incoming event increases the probability of an associated hypothesis. In this paper we introduce a system which emulates the emergent process of hypothesis generation from human observers. Basically it is a bottom up approach of fusion of events. The starting point is a matrix of correlation coefficients between pairs of events. The system builds up a network of linked events. The largest network of highly salient events is the prevailing hypothesis at a given moment. In this way the system is able to generate hypothesis not defined at start. We describe the design of the proposed system and results of testing it in a surveillance environment of aggression detection.