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    Data fusion in 3D vision using a RGB-D data via switching observation model and its application to people tracking

    Access Status
    Fulltext not available
    Authors
    Kim, D.
    Vo, Ba Tuong
    Vo, Ba-Ngu
    Date
    2013
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kim, Du Yong and Vo, Ba-Tuong and Vo, Ba-Ngu. 2013. Data fusion in 3D vision using a RGB-D data via switching observation model and its application to people tracking, in International Conference on Control, Automation and Information Sciences (ICCAIS), Nov 25-28 2013, pp. 91-96. Nha Trang, Vietnam: IEEE.
    Source Title
    2013 International Conference on Control, Automation and Information Sciences
    Source Conference
    ICCAIS 2013
    DOI
    10.1109/ICCAIS.2013.6720536
    ISBN
    978-1-4799-0569-0
    URI
    http://hdl.handle.net/20.500.11937/45983
    Collection
    • Curtin Research Publications
    Abstract

    In this paper, we propose a new method for 3D people tracking with RGB-D observations. The proposed method fuses RGB and depth data via a switching observation model. Specifically, the proposed switching observation model intelligently exploits both final detection results and raw signal intensity in a complementary manner in order to cope with missing detections. In real-world applications, the detector response to RGB data is frequently missing. When this occurs the proposed algorithm exploits the raw depth signal intensity. The fusion of detection result and raw signal intensity is integrated with the tracking task in a principled manner via the Bayesian paradigm and labeled random finite set (RFS). Our case study shows that the proposed method can reliably track people in a recently published 3D indoor data set.

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