Walking Pedestrian Detection and Classification

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 motorways. Nevertheless, not much attention has been put on image processing approaches to increase safety of pedestrians in urban environments. In this paper a method for detection, tracking, and final classification of pedestrians crossing the moving oberserver’s trajectory is suggested. Herein a combination of data and model driven approaches is realized. The initial detection process is based on a fusion of texture analysis, model-based grouping of most likely geometric features of pedestrians, and inverse-perspective mapping (binocular vision). Additionally, motion patterns of limb movements are analyzed to determine initial object hypotheses. The 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.