A Bayesian Network for Automatic Visual Crowding Estimation in Underground Stations

A system for crowding evaluation in complex environments is presented. The system acquires and processes data from a set of cameras monitoring an underground scene. The processing structure is modelled as a hierarchical Bayesian network of interacting nodes; each node aims at obtaining the probabilistic value of the number of people, detected within either local areas or the whole station, starting from suitable features extracted from images. Piece-wise linear models allow mapping from the feature value space to the number of people to be performed. The modelling algorithm, based on the Bellman Principle, is discussed. Results obtained after an extended test phase in a station of Genova’s underground are reported.

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