Heel-Strike Detection for Angle Invariant Gait Pattern Recognition

Abstract

Gait is one of the most important biometric modality for recognition in the field of computer vision. The aim of gait recognition is to identify people by analysis of motion characteristics. Heel-Strike detection is a widely used cue for gait recognition in a visual surveillance environment. Most algorithms work best when a subject walks parallel to the camera. However, in real life scenario, it is not practical to assume this. Thus, we need a view independent approach. In this thesis, given a set of silhouette images of a person walking, a viewpoint invariance method is proposed to find gait feature heel-strike. Results obtained from the simulation of CASIA Gait Database are compared with the ground truth.