People Detection and Recognition using Gait for Automated Visual Surveillance

In this paper, a computer vision system for automated visual surveillance in an unconstrained outdoor environment is described. We propose a method for tracking multiple moving objects based on shape-based feature correspondence between consecutive frames. We have explored a new approach for walking people detection and recognition based on their gait motion. The novelty of our approach is motivated by the latest research for people identification using gait. The gait signature is derived using a model-based method. The experimental results confirmed the robustness of our method to discriminate between single walking people, groups of people and vehicles with a detection rate of %100. Furthermore, the system is able to recognize walking people with a CCR of %92.