Occlusion management in distributed multi-object tracking for visual-surveillance
Fouad Bousetouane, Franck Vandewiele, Cina Motamed · Pattern Recognition and Image Analysis · 2015
This paper presents a distributed framework for multi-object tracking which deals with complex static and dynamic occlusions in visual-surveillance crowded scenes. Multiple autonomous particle filters are used for multi-object tracking in which each filter tracks a specific object. Stop-and-Go technique based on inter-blobs management, graph matching and a model of the scene is proposed for handling complex occlusions and inter-particle coalescence problems. The proposed technique is embedded into each autonomous filter to perform multi-object tracking in real time with linear complexity in terms of the number of the tracked objects. Experimental results in challengingsurveillance sequences demonstrate the robustness of the proposed framework.