Feature integration with random forests for real-time human activity recognition

Hirokatsu Kataoka, Kiyoshi Hashimoto, Yoshimitsu Aoki

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

    Abstract

    This paper presents an approach for real-time human activity recognition. Three different kinds of features (flow, shape, and a keypoint-based feature) are applied in activity recognition. We use random forests for feature integration and activity classification. A forest is created at each feature that performs as a weak classifier. The international classification of functioning, disability and health (ICF) proposed by WHO is applied in order to set the novel definition in activity recognition. Experiments on human activity recognition using the proposed framework show - 99.2% (Weizmann action dataset), 95.5% (KTH human actions dataset), and 54.6% (UCF50 dataset) recognition accuracy with a real-time processing speed. The feature integration and activity-class definition allow us to accomplish high-accuracy recognition match for the state-of-the-art in real-time.

    Original languageEnglish
    Title of host publicationSeventh International Conference on Machine Vision, ICMV 2014
    EditorsBranislav Vuksanovic, Jianhong Zhou, Antanas Verikas, Petia Radeva
    PublisherSPIE
    ISBN (Electronic)9781628415605
    DOIs
    Publication statusPublished - 2015 Jan 1
    Event7th International Conference on Machine Vision, ICMV 2014 - Milan, Italy
    Duration: 2014 Nov 192014 Nov 21

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume9445
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Other

    Other7th International Conference on Machine Vision, ICMV 2014
    CountryItaly
    CityMilan
    Period14/11/1914/11/21

    Keywords

    • Activity Recognition
    • Feature Integration
    • Random Forests

    ASJC Scopus subject areas

    • Electronic, Optical and Magnetic Materials
    • Condensed Matter Physics
    • Computer Science Applications
    • Applied Mathematics
    • Electrical and Electronic Engineering

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  • Cite this

    Kataoka, H., Hashimoto, K., & Aoki, Y. (2015). Feature integration with random forests for real-time human activity recognition. In B. Vuksanovic, J. Zhou, A. Verikas, & P. Radeva (Eds.), Seventh International Conference on Machine Vision, ICMV 2014 [944506] (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 9445). SPIE. https://doi.org/10.1117/12.2181201