Human tracking method based on improved HOG+Real AdaBoost

Daisuke Aoki, Junzo Watada

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

    1 Citation (Scopus)


    This paper proposes an object detection method that uses Histograms of Oriented Gradients (HOG) features using boosting algorithm. There has been done many research works in late years on statistical learning methods and object detection methods that associate low level of features obtained. However the proposed approach, low level of HOG features are associated by using Real AdaBoost to continuously achieve features. In this wise, it is possible to capture a shape of edge continuity, which single HOG features can't do, so highly accuracy detection is realized. This paper, to evaluate the effectiveness of the proposed method, three different experiments with different patterns are conducted for detecting humans. Moreover, a boosting classifier is used to represent the co-occurrence of HOG features appearance for detecting a human.

    Original languageEnglish
    Title of host publication2015 10th Asian Control Conference: Emerging Control Techniques for a Sustainable World, ASCC 2015
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    ISBN (Print)9781479978625
    Publication statusPublished - 2015 Sept 8
    Event10th Asian Control Conference, ASCC 2015 - Kota Kinabalu, Malaysia
    Duration: 2015 May 312015 Jun 3


    Other10th Asian Control Conference, ASCC 2015
    CityKota Kinabalu


    • Histograms of Oriented Gradients
    • Real Adaboost

    ASJC Scopus subject areas

    • Control and Systems Engineering


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