Enhanced laplacian group sparse learning with lifespan outlier rejection for visual tracking

Behzad Bozorgtabar*, Roland Goecke

*Corresponding author for this work

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


    Recently, sparse based learning methods have attracted much attention in robust visual tracking due to their effectiveness and promising tracking results. By representing the target object sparsely, utilising only a few adaptive dictionary templates, in this paper, we introduce a new particle filter based tracking method, in which we aim to capture the underlying structure among the particle samples using the proposed similarity graph in a Laplacian group sparse framework, such that the tracking results can be improved. Furthermore, in our tracker, particles contribute with different probabilities in the tracking result with respect to their relative positions in a given frame in regard to the current target object location. In addition, since the new target object can be well modelled by the most recent tracking results, we prefer to utilise the particle samples that are highly associated to the preceding tracking results. We demonstrate that the proposed formulation can be efficiently solved using the Accelerated Proximal method with just a small number of iterations. The proposed approach has been extensively evaluated on 12 challenging video sequences. Experimental results compared to the state-of-the-art methods demonstrate the merits of the proposed tracker.

    Original languageEnglish
    Title of host publicationComputer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
    EditorsDaniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang
    PublisherSpringer Verlag
    Number of pages15
    ISBN (Electronic)9783319168135
    Publication statusPublished - 2015
    Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
    Duration: 1 Nov 20145 Nov 2014

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference12th Asian Conference on Computer Vision, ACCV 2014


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