Incremental learning for visual tracking

Jongwoo Lim, David Ross, Ruei Sung Lin, Ming Hsuan Yang

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

    11 Citations (Scopus)


    Most existing tracking algorithms construct a representation of a target object prior to the tracking task starts, and utilize invariant features to handle appearance variation of the target caused by lighting, pose, and view angle change. In this paper, we present an efficient and effective online algorithm that incrementally learns and adapts a low dimensional eigenspace representation to reflect appearance changes of the target, thereby facilitating the tracking task. Furthermore, our incremental method correctly updates the sample mean and the eigenbasis, whereas existing incremental subspace update methods ignore the fact the sample mean varies over time. The tracking problem is formulated as a state inference problem within a Markov Chain Monte Carlo framework and a particle filter is incorporated for propagating sample distributions over time. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large pose and lighting changes.

    Original languageEnglish
    Title of host publicationAdvances in Neural Information Processing Systems 17 - Proceedings of the 2004 Conference, NIPS 2004
    PublisherNeural information processing systems foundation
    ISBN (Print)0262195348, 9780262195348
    Publication statusPublished - 2005 Jan 1
    Event18th Annual Conference on Neural Information Processing Systems, NIPS 2004 - Vancouver, BC, Canada
    Duration: 2004 Dec 132004 Dec 16

    Publication series

    NameAdvances in Neural Information Processing Systems
    ISSN (Print)1049-5258


    Conference18th Annual Conference on Neural Information Processing Systems, NIPS 2004
    CityVancouver, BC

    All Science Journal Classification (ASJC) codes

    • Computer Networks and Communications
    • Information Systems
    • Signal Processing


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