View-invariant 3D action recognition using spatiotemporal self-similarities from depth camera

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

    9 Citations (Scopus)

    Abstract

    The problem of viewpoint changes is an important issue in the study of human action recognition. In this paper, we propose the use of spatial features in a spatiotemporal self-similarity matrix (SSM) based on action recognition that is robust in viewpoint changes from depth sequences. The spatial features represent a discriminative density of 3D point clouds in a 3D grid. We construct the spatiotemporal SSM for the spatial features that change along with frames. To obtain the spatiotemporal SSM, we compute the Euclidean distance of each spatial feature between two frames. The spatiotemporal SSM represents similarity of human action robust in viewpoint changes. Our proposed method is robust in viewpoint changes and various length of action sequence. This method is evaluated on ACTA2 dataset containing the multi-view RGBD human action data, and MSRAction3D dataset. In the experimental validation, the spatiotemporal SSM is a good solution for the problem of viewpoint changes in a depth sequence.

    Original languageEnglish
    Title of host publicationProceedings - International Conference on Pattern Recognition
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages501-505
    Number of pages5
    ISBN (Electronic)9781479952083
    DOIs
    Publication statusPublished - 2014 Dec 4
    Event22nd International Conference on Pattern Recognition, ICPR 2014 - Stockholm, Sweden
    Duration: 2014 Aug 242014 Aug 28

    Publication series

    NameProceedings - International Conference on Pattern Recognition
    ISSN (Print)1051-4651

    Other

    Other22nd International Conference on Pattern Recognition, ICPR 2014
    Country/TerritorySweden
    CityStockholm
    Period14/8/2414/8/28

    Bibliographical note

    Publisher Copyright:
    © 2014 IEEE.

    ASJC Scopus subject areas

    • Computer Vision and Pattern Recognition

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