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    Tracking human poses in various scales with accurate appearance

    Access Status
    Fulltext not available
    Authors
    Tian, J.
    Lu, Y.
    Li, L.
    Liu, Wan-Quan
    Date
    2017
    Collection
    • Curtin Research Publications
    Type
    Journal Article
    Metadata
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    Abstract

    © 2016, Springer-Verlag Berlin Heidelberg. Building a robust and fully automatic framework for human motion tracking in 2D images and videos remains a challenging task in computer vision due to cluttered backgrounds, self-occlusions, variations of body shape and complexities of human postures. In this paper we propose a robust framework for human motion tracking without motion priors. The proposed framework builds an accurate/uncontaminated specific appearance model and then tracks the target’s postures with this specific appearance model. The main contribution of this work is a novel process to build an accurate appearance model by identifying non-target pixels and removing them. In addition, for the goal of tracking in multiple scales, a novel strategy for scale evaluation and adjustment is proposed to adaptively change the scale values during the tracking process. Experiments show that the accurate specific appearance model outperforms existing work, and the proposed tracking system is able to successfully track challenging sequences with different appearances, motions, scales and angles of view.

    Citation
    Tian, J. and Lu, Y. and Li, L. and Liu, W. 2017. Tracking human poses in various scales with accurate appearance. International Journal of Machine Learning and Cybernetics. 8 (5): pp. 1667-1680.
    Source Title
    International Journal of Machine Learning and Cybernetics
    URI
    http://hdl.handle.net/20.500.11937/61169
    DOI
    10.1007/s13042-016-0537-8
    Department
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)

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