Human Detection and Tracking in Crowded Scenes using a Fast Mean Shift Procedure

Csaba Beleznai (Advanced Computer Vision Vienna, Austria)

Detecting individual humans within groups becomes a non-trivial task when performing video surveillance in crowded scenes. We propose a novel way to detect individual humans using a fast variant of the mean shift mode seeking procedure and verifying the hypothesized configuration by a model-based validation step. The method runs in real-time. Promising results will be demonstrated for challenging image sequences.