Detection and Recognition of Human in Videos Using Adaptive Method and Neural Net

Abstract

Detection and recognition of the moving objects in dynamic environment is difficult task. This paper presents a modified framework for the detection and recognition of moving people in videos. Detection part of the proposed method consists of average background model with supportive secondary model and an adaptive threshold selection model based on Gaussian distribution. The background model used for background modelling and adaptive threshold method is used to simultaneously update the system according to environment. Then feature extraction is performed by an established human model. This human model consists of five parts with robust features to facilitate recognition process. For recognition purpose, back propagation neural network has been used as a classifier. Experimental results show the effectiveness of proposed system.

Video Surveillance System for Security Applications

“…A test image is recognized by computing the Euclidean distance and selecting the closest match. In [11], using Neural networks, certain features of human model such as area, perimeter, centroid, principal axis of inertia are selected and fed in to the classifier for training and classification. After exposure to different situations in the video, the model is updated with best possible match.…”