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

  • Ivan Laptev
  • Tony Lindeberg
Conference paper
Part of the Lecture Notes in Computer Science 2106 book series (LNCS, volume 2106)

Abstract

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.

Keywords

Feature Detection Hand Posture Hand Gesture Dissimilarity Measure Hand Tracking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Ivan Laptev
    • 1
  • Tony Lindeberg
    • 1
  1. 1.Department of Numerical Analysis and Computer ScienceComputational Vision and Active Perception Laboratory (CVAP)StockholmSweden

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