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Abstract

In recent years a lot of methods providing the ability to recognize rigid obstacles-like sedans and trucks have been developed. These methods mainly provide driving relevant information to the driver. They are able to cope reliably with scenarios on motor-ways. Nevertheless, not much attention has been given to image processing approaches to increase safety of pedestrians in traffic environments. In this paper a method for detection, tracking, and final classification of pedestrians crossing the moving observer's trajectory is suggested. Herein a combination of data and model driven approaches is realized. The initial detection process is based on a texture analysis and a model-based grouping of most likely geometric features belonging to a pedestrian on intensity images. Additionally, motion patterns of limb movements are analyzed to determine initial object hypotheses. For this tracking of the quasi-rigid part of the body is performed by different trackers that have been successfully employed for tracking of sedans, trucks, motor-bikes, and pedestrians. The final classification is obtained by a temporal analysis of the walking process.