Clustering Techniques for Human Posture Recognition: K-Means, FCM and SOM

An automated surveillance system should have the ability to recognize human behaviour and to warn security personnel of any impending suspicious activity. Human posture is one of the key aspects of analyzing human behaviour. We investigated three clustering techniques to recognize human posture. The system is first trained to recognize a pair of posture and this is repeated for three pairs of human posture. Finally the system is trained to recognize five postures together. The clustering techniques used for the purpose of our investigation included K-Means, fuzzy C-Means and Self-Organizing Maps. The results showed that K-Means and Fuzzy C-Means performed well for the three pair of posture data. However these clustering techniques gave low accuracy when we scale up the dataset to five different postures. SelfOrganizing Maps produce better recognition accuracy when tested for five postures. Key-Words: Surveillance systems, posture recognition, Clustering, K-Means, fuzzy C-Means, SelfOrganizing Maps