GMM-QNT hybrid framework for vision-based human motion analysis

The understanding of human behaviour in video is a challenging task in that the same behaviour might have several different meanings depending upon the scene and task context in which it is performed. While human seem to perform scene interpretations without effort, this is a formidable and yet unsolved task for artificial vision systems. One of the main reasons is that there exists a gap between low-level vision at signal level and high-level representation of activities at symbolic level. In this paper, we present an intelligent connection framework using Gaussian Mixture Model-based clustering (GMM) to bridge the low-level vision data and the Qualitative Normalised Templates (QNT) - a symbolic representation for human motion based on fuzzy qualitative robot kinematics, which could link the former with domain-dependent scenarios. The proposed method has been applied to the recognition of eight types of human motions and an empirical comparison with fuzzy hidden Markov-based human motion recognition system.

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