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    Online multi-object tracking via labeled random finite set with appearance learning

    Access Status
    Fulltext not available
    Authors
    Kim, Du Yong
    Date
    2017
    Type
    Conference Paper
    
    Metadata
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    Citation
    Kim, D.Y. 2017. Online multi-object tracking via labeled random finite set with appearance learning, pp. 181-186.
    Source Title
    2017 International Conference on Control, Automation and Information Sciences, ICCAIS 2017
    DOI
    10.1109/ICCAIS.2017.8217572
    ISBN
    9781538631140
    School
    School of Electrical Engineering, Computing and Mathematical Science (EECMS)
    URI
    http://hdl.handle.net/20.500.11937/66632
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. In this paper, a novel approach to online multi-object tracking is proposed via Labeled Random Finite Sets (RFS) combined with appearance learning. The Labeled RFS formulation of the multi-object state naturally accommodates a time-varying number of objects, track labels, and false positive rejection in a single Bayesian framework. The proposed algorithm exploits appearance feature information for the purpose of learning an object's appearance model, and uses this additional information in the construction an augmented likelihood which improves performance and facilitates track re-initialization. This approach enhances the baseline tracking algorithm and shows better performance with respect to mis-detections, occlusions and false track rejection. Competitive tracking results are shown compared to state-of-the-art algorithms on PETS benchmark [1] video datasets.

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