Parts-based multi-task sparse learning for visual tracking

Zhengjian Kang, Edward K. Wong

    Research output: Chapter in Book/Report/Conference proceedingConference contribution


    We present a novel parts-based multi-task sparse learning method for particle-filter-based tracking. In our method, candidate regions are divided into structured local parts which are then sparsely represented by a linear combination of atoms from dictionary templates. We consider parts in each particle as individual tasks and jointly incorporate intrinsic relationship between tasks across different parts and across different particles under a unified multi-task framework. Unlike most sparse-coding-based trackers that use holistic representation, we generate sparse coefficients from local parts, thereby allowing more flexibility. Furthermore, by introducing group sparse ℓ1,2 norm into the linear representation problem, our tracker is able to capture outlier tasks and identify partially occluded regions. The performance of the proposed tracker is empirically compared with state-of-the-art trackers on several challenging video sequences. Both quantitative and qualitative comparisons show that our tracker is superior and more robust.

    Original languageEnglish (US)
    Title of host publication2015 IEEE International Conference on Image Processing, ICIP 2015 - Proceedings
    PublisherIEEE Computer Society
    Number of pages5
    ISBN (Electronic)9781479983391
    StatePublished - Dec 9 2015
    EventIEEE International Conference on Image Processing, ICIP 2015 - Quebec City, Canada
    Duration: Sep 27 2015Sep 30 2015

    Publication series

    NameProceedings - International Conference on Image Processing, ICIP
    ISSN (Print)1522-4880


    OtherIEEE International Conference on Image Processing, ICIP 2015
    CityQuebec City


    • Multi-task learning
    • particle filter
    • parts-based model
    • sparse representation
    • visual tracking

    ASJC Scopus subject areas

    • Software
    • Computer Vision and Pattern Recognition
    • Signal Processing


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