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A key-point based approach for long-term visual tracking

Tina Strgar (2014) A key-point based approach for long-term visual tracking. EngD thesis.

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    In the thesis the problem of long-term visual tracking is addressed. The main challenges of the problem are on-line learning of the target's visual appearance, recognition of target's absence and it's redetection. A part-based tracker is proposed using local features and affine transformation. Long-term tracking is performed with tracking-by-detection, supported by optical flow in the short term. Two nested methods are used when fitting the transformation: firstly, a cluster of potential target points is defined, then the affine deformation is robustly estimated. New model features are added based on the global shape template, that is updated by the features themselves, forming a feedback-loop. The tracker is tested on two groups of sequences, the first targeting long-term and the second short-term trackers. The results are compared with the state-of-the-art methods. The performance of the tracker is comparable, though the problem of redetection should be more carefully addressed.

    Item Type: Thesis (EngD thesis)
    Keywords: computer vision, long-term visual tracking, online learning, general Hough transform, affine transformation
    Number of Pages: 52
    Language of Content: Slovenian
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Matej Kristan4053Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536123075)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 2740
    Date Deposited: 19 Sep 2014 17:39
    Last Modified: 18 Dec 2014 12:45
    URI: http://eprints.fri.uni-lj.si/id/eprint/2740

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