Technical Gestures Recognition by Set-Valued Hidden Markov Models with Prior Knowledge

  • Yann Soullard
  • Alessandro Antonucci
  • Sébastien Destercke
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 456)

Abstract

Hidden Markov models are popular tools for gesture recognition. Once the generative processes of gestures have been identified, an observation sequence is usually classified as the gesture having the highest likelihood, thus ignoring possible prior information. In this paper, we consider two potential improvements of such methods: the inclusion of prior information, and the possibility of considering convex sets of probabilities (in the likelihoods and the prior) to infer imprecise, but more reliable, predictions when information is insufficient. We apply the proposed approach to technical gestures, typically characterized by severe class imbalance. By modelling such imbalances as a prior information, we achieve more accurate results, while the imprecise quantification is shown to produce more reliable estimates.

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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Yann Soullard
    • 1
    • 3
  • Alessandro Antonucci
    • 2
  • Sébastien Destercke
    • 1
    • 3
  1. 1.Heudiasyc lab, CNRS UMR 7253 Heudiasyc, CS 60 319, 60 203Université de technologie deCompiègne cedexFrance
  2. 2.Istituto Dalle Molle di Studi Sull’Intelligenza ArtificialeManno-LuganoSwitzerland
  3. 3.Sorbonne UniversityParisFrance

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