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Detecting and tracking pedestrians accurately is essential to design efficient and robust collision avoidance systems. But traditional approaches to pedestrian detection and tracking in dense urban environments suffer from tracking failures and wrong classifications. We propose in this paper a system that recursively estimates the true outlines of every tracked target using a set of segments called ldquoAppearancerdquo. Both the state and the true contours of each target are recursively estimated and can then be used for accurate classification. We show also that accurate information on target outlines allow for a meticulous occlusions computation and an enhanced data association. The performances of this new approach is assessed through a qualitative and quantitative comparison with a state of the art pedestrian detection algorithm.