Linearization to nonlinear learning for visual tracking

Bo Ma, Hongwei Hu, Jianbing Shen*, Yuping Zhang, Fatih Porikli

*Corresponding author for this work

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

    37 Citations (Scopus)

    Abstract

    Due to unavoidable appearance variations caused by occlusion, deformation, and other factors, classifiers for visual tracking are nonlinear as a necessity. Building on the theory of globally linear approximations to nonlinear functions, we introduce an elegant method that jointly learns a nonlinear classifier and a visual dictionary for tracking objects in a semi-supervised sparse coding fashion. This establishes an obvious distinction from conventional sparse coding based discriminative tracking algorithms that usually maintain two-stage learning strategies, i.e., learning a dictionary in an unsupervised way then followed by training a classifier. However, the treating dictionary learning and classifier training as separate stages may not produce both descriptive and discriminative models for objects. By contrast, our method is capable of constructing a dictionary that not only fully reflects the intrinsic manifold structure of the data, but also possesses discriminative power. This paper presents an optimization method to obtain such an optimal dictionary, associated sparse coding, and a classifier in an iterative process. Our experiments on a benchmark show our tracker attains outstanding performance compared with the state-of-the-art algorithms.

    Original languageEnglish
    Title of host publication2015 International Conference on Computer Vision, ICCV 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages4400-4407
    Number of pages8
    ISBN (Electronic)9781467383912
    DOIs
    Publication statusPublished - 17 Feb 2015
    Event15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile
    Duration: 11 Dec 201518 Dec 2015

    Publication series

    NameProceedings of the IEEE International Conference on Computer Vision
    Volume2015 International Conference on Computer Vision, ICCV 2015
    ISSN (Print)1550-5499

    Conference

    Conference15th IEEE International Conference on Computer Vision, ICCV 2015
    Country/TerritoryChile
    CitySantiago
    Period11/12/1518/12/15

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