Gait-Based Pedestrian Detection for Automated Surveillance

Abstract

In this paper, we explore a new approach for walking pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on gait motion as the rhythm of the footprint pattern of walking people is considered the stable and characteristic feature for the classification of moving objects. The novelty of our approach is motivated by the latest research for people identification using gait. The experimental results confirmed the robustness of our method to discriminate between single walking subject, groups of people and vehicles with a detection rate of %100. Furthermore, the results revealed the potential of our method to extend visual surveillance systems to recognize walking people.