Shape Analysis and Classification: Theory and Practice
Shape Analysis and Classification: Theory and Practice
Posture Estimation in Visual Surveillance of Archaeological Sites
AVSS '03 Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
Introduction to Machine Learning (Adaptive Computation and Machine Learning)
ICETET '08 Proceedings of the 2008 First International Conference on Emerging Trends in Engineering and Technology
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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. Self-Organizing Maps produce better recognition accuracy when tested for five postures.