HUMAN ACTION RECOGNITION USING DEEP LEARNING AND OPENCV

Abstract

With the rise of terrorist round the world, human identification research has become a asked for area of research. Unlike standard biometric recognition techniques, gait recognition may be a non-intrusive technique. Both data collection and classification processes are often avoided a subject’s cooperation. During this work, we proposed a replacement model-based gait recognition technique called postured based gait recognition. The datasets are obtained by analyzing the freestyle walk of a private and that we have trained a machine learning model with these datasets so as to predict who the individual is. By storing the model we will successfully predict who a personal is by giving a picture or video as input. The proposed system performs well for varying class sizes and may help implementing a reliable security closedcircuit television.