Markerless multi-view articulated pose estimation using adaptive hierarchical particle swarm optimisation

Spela Ivekovic, Vijay John, Emanuele Trucco

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    5 Citations (Scopus)


    In this paper, we present a new adaptive approach to multi-view markerless articulated human body pose estimation from multi-view video sequences, using Particle Swarm Optimisation (PSO). We address the computational complexity of the recently developed hierarchical PSO (HPSO) approach, which successfully estimated a wide range of different motion with a fixed set of parameters, but incurred an unnecessary overhead in computational complexity. Our adaptive approach, called APSO, preserves the black-box property of the HPSO in that it requires no parameter value input from the user. Instead, it adaptively changes the value of the search parameters online, depending on the quality of the pose estimate in the preceding frame of the sequence. We experimentally compare our adaptive approach with HPSO on four different video sequences and show that the computational complexity can be reduced without sacrificing accuracy and without requiring any user input or prior knowledge about the estimated motion type.

    Original languageEnglish
    Title of host publicationApplications of Evolutionary Computation
    Subtitle of host publicationEvoApplicatons 2010: EvoCOMPLEX, EvoGAMES, EvoIASP, EvoINTELLIGENCE, EvoNUM, and EvoSTOC, Istanbul, Turkey, April 7-9, 2010, Proceedings, Part I
    EditorsC. DiChic, S. Cagoni, C. Cotta, M. Ebner, A. Ekart, A. I. EsparciaAlcazar, C. K. Goh, J. J. Merelo, F. Neri, M. Preuss, J. Togelius, G. N. Yannakakis
    Place of PublicationBerlin
    Number of pages10
    ISBN (Electronic)9783642122392
    ISBN (Print)9783642122385
    Publication statusPublished - 2010
    EventEvoApplications 2010: European Conference on the Applications of Evolutionary Computation - Istanbul, Turkey
    Duration: 7 Apr 20109 Apr 2010

    Publication series

    NameLecture notes in computer science
    ISSN (Print)0302-9743


    ConferenceEvoApplications 2010: European Conference on the Applications of Evolutionary Computation
    Internet address



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