Integration of multisensor data for overcrowding estimation

A system for interpretation of complex scenes is presented. The main characteristics of the system are: virtual multisensor input, knowledge based and multilevel architecture, and intensional approach. The specific application performed by the system is crowding evaluation in underground station environment, in order to detect dangerous situation, by using optical sensors. The multilevel architecture of the system is modelled as a probabilistic network of passing-message nodes. Each node corresponds to a virtual distributed processor that is used to obtain the probabilistic value of the locally detected crowding level. The network updating mechanism is presented. By using several Low Level algorithms suitable features are extracted from images. The virtual sensor models are described.