Motion-based recognition of pedestrians

In this paper we present an algorithm for recognizing walking pedestrians in sequences of color images taken from a moving camera. The recognition is based on the characteristic motion of the legs of a pedestrian walking parallel to the image plane. Each image is segmented into region-like image parts by clustering pixels in a combined color/position feature space. The proposed clustering technique implies matching of corresponding clusters in consecutive frames and therefore allows clusters to be tracked over a sequence of images. Based on the observation of clusters over time a two-stage classifier extracts those clusters which most likely represent the legs of pedestrians. A fast polynomial classifier performs a rough preselection of clusters by evaluating temporal changes of a shape-dependent clusters feature. The final classification is done by a time delay neural network (TDNN) with spatio-temporal receptive fields.