Beyond local search: Tracking objects everywhere with instance-specific proposals

Gao Zhu, Fatih Porikli, Hongdong Li

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

    211 Citations (Scopus)

    Abstract

    Most tracking-by-detection methods employ a local search window around the predicted object location in the current frame assuming the previous location is accurate, the trajectory is smooth, and the computational capacity permits a search radius that can accommodate the maximum speed yet small enough to reduce mismatches. These, however, may not be valid always, in particular for fast and irregularly moving objects. Here, we present an object tracker that is not limited to a local search window and has ability to probe efficiently the entire frame. Our method generates a small number of 'high-quality' proposals by a novel instance-specific objectness measure and evaluates them against the object model that can be adopted from an existing tracking-by-detection approach as a core tracker. During the tracking process, we update the object model concentrating on hard false-positives supplied by the proposals, which help suppressing distractors caused by difficult background clutters, and learn how to re-rank proposals according to the object model. Since we reduce significantly the number of hypotheses the core tracker evaluates, we can use richer object descriptors and stronger detector. Our method outperforms most recent state-of-the-art trackers on popular tracking benchmarks, and provides improved robustness for fast moving objects as well as for ultra lowframerate videos.

    Original languageEnglish
    Title of host publicationProceedings - 29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    PublisherIEEE Computer Society
    Pages943-951
    Number of pages9
    ISBN (Electronic)9781467388504
    DOIs
    Publication statusPublished - 9 Dec 2016
    Event29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016 - Las Vegas, United States
    Duration: 26 Jun 20161 Jul 2016

    Publication series

    NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
    Volume2016-December
    ISSN (Print)1063-6919

    Conference

    Conference29th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
    Country/TerritoryUnited States
    CityLas Vegas
    Period26/06/161/07/16

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