Walking pedestrian recognition

In recent years many methods providing the ability to recognize rigid obstacles sedans and trucks have been developed. These methods provide the driver with relevant information. They are able to cope reliably with scenarios on motorways. Nevertheless, not much attention has been given to image processing approaches to increase the safety of pedestrians in urban environments. In this paper a method for the detection, tracking, and final recognition of pedestrians crossing the moving oberserver’s trajectory is suggested. A combination of dataand model-driven approaches is realized. The initial detection process is based on a fusion of texture analysis, model-based grouping of, most likely, the geometric features of pedestrians, and inverseperspective 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 algorithms that have been successfully employed for the tracking of sedans, trucks, motorbikes, and pedestrians. The final classification is obtained by a temporal analysis of the walking process. Keywords— image analysis, inverse perspective mapping, Hausdorff-Distance, texture, spatio-temporal pattern, data fusion, system architecture