A novel framework is proposed under which robust matching and tracking of a 3D skeleton model of a human body from multiple views can be performed We propose a method for measuring the ambiguity of 2D measurements provided by each view. The ambiguity measurement is then used for selecting the best view for the most accurate match and tracking. A hybrid 2D-3D representation is chosen for modelling human body poses. The hybrid model is learnt using hierarchical principal component analysis. The CONDENSATION algorithm is used to robustly track and match 3D skeleton models in individual views.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Tracking hybrid 2D-3D human models from multiple views


    Contributors:
    Eng-Jon Ong (author) / Shaogang Gong (author)


    Publication date :

    1999-01-01


    Size :

    383783 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Tracking Hybrid 2D-3D Human Models from Multiple Views

    Ong, E. / Gong, S. / IEEE Computer Society | British Library Conference Proceedings | 1999


    Markerless tracking of complex human motions from multiple views

    Kehl, R. / Gool, L. V. | British Library Online Contents | 2006


    Full body tracking from multiple views using stochastic sampling

    Kehl, R. / Bray, M. / Van Gool, L. | IEEE | 2005


    Modelling and tracking articulated motion from multiple camera views

    Ringer, Maurice / Lasenby, Joan | TIBKAT | 2001


    Tracking across Multiple Cameras with Disjoint Views

    Javed, O. / Rasheed, Z. / Shafique, K. et al. | British Library Conference Proceedings | 2003