Human tracking: a state-of-art survey
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II
On line background modeling for moving object segmentation in dynamic scenes
Multimedia Tools and Applications
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In the development of natural interaction for systems, the trend of using vision toward translating human actions into instruction symbols is arising for the recognition of non-verbal communication channels. We propose an Adaptive Vision-based Attentive Tracker (AVAT) to track human intended communicational actions with attentive zooming capability as well as an exploration function for model updating. The AVAT isolates such human intended actions from the ordinary walking behavior based on an algorithm with two sub-processes: one is for modeling the movement of human body parts as the environment using HMMs (Hidden Markov Models) algorithm, and the other is for learning the model of the tracker's action using a model-based TD (Temporal Difference) algorithm. In the paper, we describe the integration of the two algorithms and then derive the model updating formulas from the newly optimized TD policies. An experimental result of isolating the human sign action during his natural walking motion is shown for demonstrating the feasibility of our system. Identification of the sign gesture context using a confirmation method using wavelet analysis which provides rewards for optimizing the tracker's action models.