Asymmetric totally-corrective boosting for real-time object detection

Peng Wang*, Chunhua Shen, Nick Barnes, Hong Zheng, Zhang Ren

*Corresponding author for this work

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

    4 Citations (Scopus)


    Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stage-wise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.

    Original languageEnglish
    Title of host publicationComputer Vision, ACCV 2010 - 10th Asian Conference on Computer Vision, Revised Selected Papers
    Number of pages13
    EditionPART 1
    Publication statusPublished - 2011
    Event10th Asian Conference on Computer Vision, ACCV 2010 - Queenstown, New Zealand
    Duration: 8 Nov 201012 Nov 2010

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    NumberPART 1
    Volume6492 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference10th Asian Conference on Computer Vision, ACCV 2010
    Country/TerritoryNew Zealand


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