Using decision trees to build an event recognition framework for automated visual surveillance
Cédric Simon, Jérôme Meessen, Christophe De Vleeschouwer · Digital Access to Libraries (Université catholique de Louvain (UCL), l'Université de Namur (UNamur) and the Université Saint-Louis (USL-B)) · 2008
This paper presents a classifier-based approach to recognize possibly sophisticated events in video surveillance. The aim of this work is to propose a flexible and generic event recognition system that can be used in a real world context. Our system uses the ensemble of randomized trees procedure to model each event as a sequence of structured activity patterns, without using any tracking method. Experimental results demonstrate the robustness of the system toward artifacts and passer-by, and the effectiveness of its framework for event recognition applications in visual surveillance.