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 2011 IEEE International Conference on Computer Vision Workshops, ICCV Workshops 2011 tenutosi a Barcelona, esp nel Nov, 2011) [10.1109/ICCVW.2011.6130469].
Multi-view people surveillance using 3D information
BALTIERI, DAVIDE;VEZZANI, Roberto;CUCCHIARA, Rita;
2011
Abstract
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
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