Online appearance learning for 3D articulated human tracking

Timothy J. Roberts, Stephen J. McKenna, Ian W. Ricketts

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

    3 Citations (Scopus)

    Abstract

    A human appearance modelling framework where colour distributions are associated with surface regions on an articulated body model is presented. In general, these distributions are unknown, multi-modal and changing in time. We therefore propose using recursively updated histograms to represent them. For a certain pose, a set of histograms may be collected and a likelihood constructed based on the histograms' similarity with the previously learned histograms. To ease histogram estimation and improve computational efficiency, a merging and splitting algorithm is derived which groups surface regions based upon histogram similarity and prior knowledge of clothing layout. An investigation of the behaviour of this likelihood shows it to be broad, smooth and peaked around the correct location, a good candidate for coarse sampling and gradient-based search methods. We show how conditioning the likelihood to maximise foreground usage reduces secondary maxima. Finally, we present results from tracking a challenging sequence.
    Original languageEnglish
    Title of host publicationProceedings. 16th International Conference on Pattern Recognition 2002.
    EditorsIra Kasturi, D. Laurendeau, C. Suen
    Place of PublicationCalifornia
    PublisherIEEE Computer Society
    Pages424-428
    Number of pages5
    Volume1
    ISBN (Print)076951695X
    DOIs
    Publication statusPublished - Aug 2002
    Event16th International Conference on Pattern Recognition - Quebec, Canada
    Duration: 11 Aug 200215 Aug 2002
    http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8091

    Conference

    Conference16th International Conference on Pattern Recognition
    CountryCanada
    CityQuebec
    Period11/08/0215/08/02
    Internet address

    Fingerprint Dive into the research topics of 'Online appearance learning for 3D articulated human tracking'. Together they form a unique fingerprint.

    Cite this