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    A robust framework for 2D human pose tracking with spatial and temporal constraints

    Access Status
    Fulltext not available
    Authors
    Tian, J.
    Li, Ling
    Liu, Wan-Quan
    Date
    2015
    Type
    Conference Paper
    
    Metadata
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    Citation
    Tian, J. and Li, L. and Liu, W. 2015. A robust framework for 2D human pose tracking with spatial and temporal constraints: Institute of Electrical and Electronics Engineers Inc..
    Source Title
    2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014
    DOI
    10.1109/DICTA.2014.7008091
    ISBN
    9781479954094
    School
    Department of Computing
    URI
    http://hdl.handle.net/20.500.11937/32211
    Collection
    • Curtin Research Publications
    Abstract

    We work on the task of 2D articulated human pose tracking in monocular image sequences, an extremely challenging task due to background cluttering, variation in body appearance, occlusion and imaging conditions. Most of current approaches only deal with simple appearance and $adjacent$ body part dependencies, especially the Gaussian tree-structured priors assumed over body part connections. Such prior makes the part connections independent to image evidence and in turn severely limits accuracy. Building on the successful pictorial structures model, we propose a novel framework combining an image-conditioned model that incorporates higher order dependencies of multiple body parts. In order to establish the conditioning variables, we employ the effective poselet features. In addition to this, we introduce a full body detector as the first step of our framework to reduce the search space for pose tracking. We evaluate our framework on two challenging image sequences and conduct a series of comparison experiments to compare the performance with another two approaches. The results illustrate that the proposed framework in this work outperforms the state-of-the-art 2D pose tracking systems.

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