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Improved robust part-based model for visual object tracking

Alan Lukežič (2015) Improved robust part-based model for visual object tracking. Prešeren awards for students.

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    This thesis addresses short-term visual object tracking by deformable parts models (DPM). The DPMs show a great potential in addressing non-rigid object deformations and self-occlusions, but according to recent benchmarks, they often lag behind the holistic approaches, which model an object with a single appearance model. The reason is that potentially large number of parameters in constellation needs to be estimated for target localization and simplifications of the constellation topology are often assumed to make the inference tractable. Furthermore, the visual model and geometric constraints are usually combined in an ad-hoc fashion. In contrast to related approaches, we present a generative model that jointly treats contributions of the visual and of the geometric model as a single physics-based spring system with a convex energy function. An efficient optimization method is proposed for this dual form that allows MAP inference of a fully-connected constellation model. The proposed optimization method is compared to the existing optimization approach and outperforms it in terms of stability and efficiency. In the thesis we propose a part-based tracker that combines two visual representations of the target, i.e., coarse and mid-level representation. The proposed optimization method is used for target localization on the mid-level representation. The resulting tracker is rigorously analyzed on a highly challenging VOT2014 benchmark, it outperforms the related part-based and holistic trackers including the winner of the VOT2014 challenge and runs in real-time. The design of the proposed tracker is analyzed by an analysis of each component of the tracker.

    Item Type: Thesis (Prešeren awards for students)
    Keywords: computer vision, visual object tracking, deformable-part models, correlation filters, spring systems
    Number of Pages: 86
    Language of Content: English
    Mentor / Comentors:
    Name and SurnameIDFunction
    doc. dr. Matej Kristan4053Mentor
    Link to COBISS: http://www.cobiss.si/scripts/cobiss?command=search&base=51012&select=(ID=1536672707)
    Institution: University of Ljubljana
    Department: Faculty of Computer and Information Science
    Item ID: 3685
    Date Deposited: 19 Dec 2016 09:55
    Last Modified: 09 Feb 2017 13:08
    URI: http://eprints.fri.uni-lj.si/id/eprint/3685

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