In this paper we introduce a novel surveillance system, which uses 3D information extracted from multiple cameras to detect, track and re-identify people. The detection method is based on a 3D Marked Point Process model using two pixel-level features extracted from multi-plane projections of binary foreground masks, and uses a stochastic optimization framework to estimate the position and the height of each person. We apply a rule based Kalman-filter tracking on the detection results to find the object-to-object correspondence between consecutive time steps. Finally, a 3D body model based long-term tracking module connects broken tracks and is also used to re-identify people
Multi-view people surveillance using 3D information / Baltieri, Davide; Vezzani, Roberto; Cucchiara, Rita; A., Utasi; C., Benedek; T., Sziranyi. - STAMPA. - (2011), pp. 1817-1824. ((Intervento presentato al convegno 11th International Workshop on Visual Surveillance tenutosi a Barcelona, Spain nel Nov, 2011.