AUTOMATIC CLASSIFICATION OF GAIT PATTERNS BY USING HYBRID FEATURES AND K-NEAREST NEIGHBOR CLASSIFIER

摘要

Human gait, which is a new biometric aimed to recognize individuals by the way they walk have come to play an increasingly important role in visual surveillance applications. In this paper a novel hybrid holistic approach is proposed to show how behavioural walking characteristics can be used to recognize unauthorized and suspicious persons when they enter a surveillance area. Initially background is modelled from the input video captured from cameras deployed for security and the foreground moving object in the individual frames are segmented using the background subtraction algorithm. Then gait representing spatial, temporal and wavelet components are extracted and fused for training and testing multi class k-Nearest Neighbour model (kNN). The proposed system is evaluated using side view videos of NLPR database and experimental results demonstrate that the proposed system achieves a pleasing recognition rate.