Comparison of supervised and unsupervised learning classifiers for human posture recognition

Human posture recognition is gaining increasing attention in the fields of artificial intelligence and 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 comprehensive problem of video sequence interpretation. In this paper, an intelligent human posture recognition system in video sequences is proposed. Firstly, the system was trained and evaluated to classify five different human postures using both supervised and unsupervised learning classifiers. The supervised classifier used was Multilayer Perceptron Feedforward Neural Networks (MLP) whilst for unsupervised learning classifiers, Self Organizing Maps (SOM), Fuzzy C Means (FCM) and K Means have been employed. Results indicate that MLP performs (96% accuracy) much better than SOMs, FCM and K Means which give accuracies of 86%, 33% and 31% respectively. Secondly, all the classifiers were then trained and evaluated again to classify two postures. With only 2 postures, the accuracies of all the classifiers have increased dramatically, especially for unsupervised classifiers. This shows that supervised learning classifiers are superior to unsupervised ones for the task of human posture recognition and that the unsupervised classifiers do not learn very well for cases where a lot of postures have to be learnt as compared to the supervised learning classifier which gives high accuracy in either case.