Tracking of Multi-state Hand Models Using Particle Filtering and a Hierarchy of Multi-scale Image Features

This paper presents an approach for simultaneous tracking and recognition of hierarchical object representations in terms of multiscale image features. A scale-invariant dissimilarity measure is proposed for comparing scale-space features at different positions and scales. Based on this measure, the likelihood of hierarchical, parameterized models can be evaluated in such a way that maximization of the measure over different models and their parameters allows for both model selection and parameter estimation. Then, within the framework of particle filtering, we consider the area of hand gesture analysis, and present a method for simultaneous tracking and recognition of hand models under variations in the position, orientation, size and posture of the hand. In this way, qualitative hand states and quantitative hand motions can be captured, and be used for controlling different types of computerised equipment.

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