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24 October 2005 People detection in crowded scenes using active contour models and texture analysis
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A vision system designed to detect people in complex backgrounds is presented. The purpose of the proposed algorithms is to allow the identification and tracking of single persons under difficult conditions - in crowded places, under partial occlusion and in low resolution images. In order to detect people reliably, we combine different information channels from video streams. Most emphasis for the initialization of trajectories and the subsequent pedestrian recognition is placed on the detection of the head-shoulder contour. In the first step a simple and fast shape model selects promising candidates, then a local active shape model is matched against the gradients found in the image with the help of a cost function. Texture analysis in the form of co-occurrence features ensures that shape candidates form coherent trajectories over time. In order to reduce the amount of false positives and to become more robust, a pattern analysis step based on Eigenimage analysis is presented. The cues which form the basis of pedestrian detection are integrated into a tracking algorithm which uses the shape information for initial pedestrian detection and verification, propagates positions into new frames using local motion and matches pedestrians with the help of texture information.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Oliver Sidla, Jean P. Andreu, Lucas Paletta, and Yuriy Lypetskyy "People detection in crowded scenes using active contour models and texture analysis", Proc. SPIE 6006, Intelligent Robots and Computer Vision XXIII: Algorithms, Techniques, and Active Vision, 600606 (24 October 2005);


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