Vision-based human action recognition in surveillance videos using motion projection profile features

Abstract

Human Action Recognition (HAR) is a dynamic research area in pattern recognition and artificial Intelligence. The area of human action recognition consistently focuses on changes in the scene of a subject with reference to time, since motion information can prudently depict the action. This paper depicts a novel framework for action recognition based on Motion Projection Profile (MPP) features of the difference image, representing various levels of a person’s posture. The motion projection profile features consist of the measure of moving pixel of each row, column and diagonal (left and right) of the difference image and gives adequate motion information to recognize the instantaneous posture of the person. The experiments are carried out using WEIZMANN and AUCSE datasets and the extracted features are modeled by the GMM classifier for recognizing human actions. In the experimental results, GMM exhibit effectiveness of the proposed method with an overall accuracy rate of 94.30% for WEIZMANN dataset and 92.49% for AUCSE dataset.