  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 support vector machine models
(SVM). The proposed system is evaluated using side view videos of NLPR
database. The experimental results demonstrate that the proposed system
achieves a pleasing recognition rate and also the results indicate that the
classification ability of SVM with Radial Basis Function (RBF) is better than
with other kernel functions.
