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Motion recognition using spatio-temporal random walks in sequence of 2D motion-related measurements

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  • Author(s): Fablet, Ronan; Bouthemy, Patrick
  • Source:
    Proceedings ICIP 2001 : IEEE International Conference on Image Processing, October 7-10, Thessalonique, Greece ; ICIP 2001 : IEEE International Conference on Image Processing, October 7-10, Thessalonique, Greece ; https://hal.science/hal-02283280 ; ICIP 2001 : IEEE International Conference on Image Processing, October 7-10, Thessalonique, Greece, Oct 2001, Thessalonique, Greece. pp.652 - 655, ⟨10.1109/ICIP.2001.958203⟩
  • Subject Terms:
    [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
  • Document Type:
    conference object
  • Language:
    English
  • Additional Information
    • Contributors:
      Département Signal et Communications (SC); Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom Paris (IMT); Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA); Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes); Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-Télécom Bretagne-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)
    • Publication Information:
      HAL CCSD
    • Publication Date:
      2001
    • Collection:
      Archives ouvertes Hal IMT Atlantique
    • Subject Terms:
      Thessalonique; Greece
    • Abstract:
      International audience ; This paper describes an original approach for non parametric motion analysis in image sequences. It relies on a statistical modeling of distributions of local motion-related measurements, computed over image sequences, resulting from spatio-temporal random walks. It handles in a single probabilistic framework both spatial and temporal properties of motion content. The important feature of our method is to make feasible the exact computation of conditional likelihood functions. We have carried out motion recognition experiments over a large set of real image sequences comprising various motion types.
    • Relation:
      hal-02283280; https://hal.science/hal-02283280; https://hal.science/hal-02283280/document; https://hal.science/hal-02283280/file/RFicip2001.pdf
    • Accession Number:
      10.1109/ICIP.2001.958203
    • Online Access:
      https://hal.science/hal-02283280
      https://hal.science/hal-02283280/document
      https://hal.science/hal-02283280/file/RFicip2001.pdf
      https://doi.org/10.1109/ICIP.2001.958203
    • Rights:
      info:eu-repo/semantics/OpenAccess
    • Accession Number:
      edsbas.A32B76D9