Causal Hidden Markov Model for view independent multiple silhouettes posture recognition

Posture language is rich in ways for individuals to express a variety of desire, feelings and thoughts. Recognizing human posture via computer is a challenging task as it involved multiple issues ranging from image, recognition algorithm and system resources. This proposed work aimed to solve viewpoint variation issue through causal topology design Hidden Markov Model (HMM) for view independent multiple silhouettes posture recognition. It duplicated the human ability in perceiving an event correctly although there is ambiguity and insufficient information. In analogy, the proposed work utilized causality to perceive an event with a determined set of cameras; such scenario allows flexibility for the object to locate anywhere. The proposed work applied the characteristic view determination approach to deduce the minimal set of viewpoint required on the human object in representing a posture; and the dynamic topology estimation method to result a causal HMM. The outcome of the causal HMM demonstrated significant improvement in reducing the supervised training data to represent the posture and provided comparable recognition accuracy for the given test data.