Density evaluation and tracking of multiple objects from image sequences

The architecture of a distributed vision system (DVS) based on a combination of multiple modules of standard and extended Kalman filters is presented. It exploits a representation of static and dynamic knowledge for estimation purposes. Spatial constraints describe how observed image features lead to estimate parameters (i.e., in the present application, the density and position of monitored people in the monitored scene); time constraints are used to describe knowledge on dynamic evolution of the mentioned estimated variables. Using dynamic knowledge allows the system to track groups of people, dynamically interacting each others, on the image plane over time. Experimental results, deriving from an extensive test phase carried out on real-life images of an underground station, confirm that integration of different spatial and temporal constraints is an efficient approach for optimizing parameter estimation in DVSs.