Human posture recognition and classification

Abstract

Human posture recognition is gaining increasing attention in the field of computer vision due to its promising applications in the areas of personal health care, environmental awareness, human-computer-interaction and surveillance systems. Human posture recognition in video sequences is a challenging task which is part of the more general problem of video sequence interpretation. This paper presents a novel an intelligent human posture recognition system for video surveillance using a single static camera. The training and testing were performed using four different classifiers. The recognition rates (accuracies) of those classifiers were then compared and results indicate that MLP gives the highest recognition rate. Moreover, results show that supervised learning classifiers tend to perform better than unsupervised classifiers for the case of human posture recognition. Furthermore, for each individual classifier, the recognition rate has been found to be proportional to the number of postures trained and evaluated. Performance comparisons between the proposed systems and existing systems were also carried out.