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MRF-based multi-view action recognition using sensor networks

Li, Haitao

International journal of sensor networks. Volume 23:Number 3 (2017); pp 201-209 -- Inderscience Enterprises Ltd

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  • Title:
    MRF-based multi-view action recognition using sensor networks
  • Author: Li, Haitao
  • Found In: International journal of sensor networks. Volume 23:Number 3 (2017); pp 201-209
  • Journal Title: International journal of sensor networks
  • Subjects: Sensor networks--Periodicals; action recognition--MRF--Markov random fields--multi-view actions--sensor networks--video surveillance--position distribution--posture sequences--posture features--visual perception--video images--simulation; Dewey: 681.2
  • Rights: legaldeposit
  • Publication Details: Inderscience Enterprises Ltd
  • Abstract:

    Action recognition has become an active area of research in the field of video surveillance. In this paper, a local space-time constraint Markov random fields (MRFs) model is proposed for the recognition of multi-view action based on the posture's articulation points from the sensor networks in the smart family space. The position distribution under different views and the time continuity of the posture sequence are used as the random field to label the corresponding action classes. Experimental results show that the proposed model can accurately recognise the actions of objects under multi-views in the family environment and requires low running time.


  • Identifier: System Number: LDEAvdc_100074489631.0x000001; Journal ISSN: 1748-1279
  • Publication Date: 2017
  • Physical Description: Electronic
  • Shelfmark(s): ELD Digital store

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