Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition

Hirokatsu Kataoka, Kiyoshi Hashimoto, Kenji Iwata, Yutaka Satoh, Nassir Navab, Slobodan Ilic, Yoshimitsu Aoki

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

    9 Citations (Scopus)

    Abstract

    In this paper we propose a novel feature descriptor Extended Co-occurrence HOG (ECoHOG) and integrate it with dense point trajectories demonstrating its usefulness in fine grained activity recognition. This feature is inspired by original Co-occurrence HOG (CoHOG) that is based on histograms of occurrences of pairs of image gradients in the image. Instead relying only on pure histograms we introduce a sum of gradient magnitudes of co-occurring pairs of image gradients in the image. This results in giving the importance to the object boundaries and straightening the difference between the moving foreground and static background. We also couple ECoHOG with dense point trajectories extracted using optical flow from video sequences and demonstrate that they are extremely well suited for fine grained activity recognition. Using our feature we outperform state of the art methods in this task and provide extensive quantitative evaluation.

    Original languageEnglish
    Title of host publicationComputer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers
    EditorsDaniel Cremers, Hideo Saito, Ian Reid, Ming-Hsuan Yang
    PublisherSpringer Verlag
    Pages336-349
    Number of pages14
    ISBN (Electronic)9783319168135
    DOIs
    Publication statusPublished - 2015 Jan 1
    Event12th Asian Conference on Computer Vision, ACCV 2014 - Singapore, Singapore
    Duration: 2014 Nov 12014 Nov 5

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9007
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Other

    Other12th Asian Conference on Computer Vision, ACCV 2014
    CountrySingapore
    CitySingapore
    Period14/11/114/11/5

    ASJC Scopus subject areas

    • Theoretical Computer Science
    • Computer Science(all)

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  • Cite this

    Kataoka, H., Hashimoto, K., Iwata, K., Satoh, Y., Navab, N., Ilic, S., & Aoki, Y. (2015). Extended co-occurrence HOG with dense trajectories for fine-grained activity recognition. In D. Cremers, H. Saito, I. Reid, & M-H. Yang (Eds.), Computer Vision - ACCV 2014 - 12th Asian Conference on Computer Vision, Revised Selected Papers (pp. 336-349). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9007). Springer Verlag. https://doi.org/10.1007/978-3-319-16814-2_22