Spatio-temporal descriptor for abnormal human activity detection

Fam Boon Lung, Mohamed Hisham Jaward, Jussi Paavo Samuli Parkkinen

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    6 Citations (Scopus)

    Abstract

    There has been an increased interest in the field of abnormal human activity detection to find a good descriptor with a lower computational cost. In this paper, we propose such a Spatio-Temporal Descriptor (STD) based on spatio-temporal features of an image sequence. Proposed descriptor is based on a texture map, known as Spatio-Temporal Texture Map (STTM) and is based on 3-dimensional Harris function. It is able to capture subtle variations in the spatio-temporal domain. Performance of the STD was illustrated with a mixture of Gaussian Hidden Markov Model (HMM) to show its potential for more complex modeling. Proposed algorithm was evaluated with UCSD dataset that has abnormal events that are not staged such as biker, skater, cart activities etc. Compared to other state of the art descriptors that are used with the same dataset, our proposed descriptor shows competitive performance with a lower computational cost.
    Original languageEnglish
    Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
    EditorsNorimichi Ukita, Eigo Segawa, Norichika Yui
    Place of PublicationNew Jersey USA
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages471 - 474
    Number of pages4
    ISBN (Print)9784901122146
    DOIs
    Publication statusPublished - 2015
    EventMachine Vision Applications 2015 - Tokyo Japan, Tokyo, Japan
    Duration: 18 May 201522 May 2015
    Conference number: 14

    Conference

    ConferenceMachine Vision Applications 2015
    Abbreviated titleMVA
    CountryJapan
    CityTokyo
    Period18/05/1522/05/15
    Other14th IAPR International Conference on Machine Vision Applications, MVA 2015

    Cite this

    Lung, F. B., Jaward, M. H., & Parkkinen, J. P. S. (2015). Spatio-temporal descriptor for abnormal human activity detection. In N. Ukita, E. Segawa, & N. Yui (Eds.), Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 471 - 474). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/MVA.2015.7153233