Using Gait Features for Improving Walking People Detection

In this paper, we explore a new approach for enriching the HoG method for pedestrian detection in an unconstrained outdoor environment. The proposed algorithm is based on using gait motion since the rhythmic footprint pattern for walking people is considered the stable and characteristic feature for the detection of walking people. 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 enhance HoG to detect walking people as well as 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 be used in visual surveillance systems for identity tracking over different camera views.