Performance Analysis for Human Gait Recognition from different Viewpoints

Abstract

Human gait, a new biometric aimed at recognizing people based on how they walk, has become increasingly important in visual surveillance application. However, one camera at a single view point gait data has commonly been explored, this has not always sufficient enough in the environment of deployment. This research proposes gait analysis as a solution for subjects’ identification across a network of cameras from different viewpoints. Gait signature of a person is created from Temporal and spatial metrics extracted from modal, such as length of trunk, shin and deviation in the limb angles or the amplitude of a person’s walking pattern and these are transformed into a self-similarity matrix. The method of spatio-temporal correlation is to detect the human gait in successive video sequences. Performance evaluation of the system was carried out with CMU (Carnegie Mellon University) Motion of Body (MoBo) database. The results reveal that gait performance analysis of the proposed system is possible even without knowing the position of the camera or the stance of the subject. This research shows that the derived gait parameters suggest that gait may be successfully employed for individuals' identification with cameras surveillance in different view point scenario which resulted in an average recognition rate of 73.3% for persons walking from right camera viewpoints, 80.0% from persons walking from the left camera viewpoint, 60.0% from persons walking from the rare camera viewpoint and 66.67% from persons walking from the front camera viewpoint. This implies that the system performs better from the left camera viewpoint.