A Comparison Of Posture Recognition Using Supervised And Unsupervised Learning Algorithms
Maleeha Kiran, Chee Seng Chan, Weng Kin Lai, Kyaw Kyaw Hitke Ali, Othman Omran Khalifa · 2010
Recognition of human posture is one step in the process of analyzing human behaviour. However, it is an ill-defined problem due to the high degree of freedom exhibited by the human body. In this paper, we study both supervised and unsupervised learning algorithms to recognise human posture in image sequences. In particular, we are interested in a specific set of postures which are representative of typical applications found in video analytics. The algorithms chosen for this paper are Kmeans, artificial neural network, self organizing maps and particle swarm optimization. Experimental results have shown that the supervised learning algorithms outperform the unsupervised learning algorithms in terms of the number of correctly classified postures. Our future work will focus on detecting abnormal behaviour based on these recognised static postures.