Evaluation of vision-based human activity recognition in dense trajectory framework

Hirokatsu Kataoka, Yoshimitsu Aoki, Kenji Iwata, Yutaka Satoh

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

    3 Citations (Scopus)

    Abstract

    Activity recognition has been an active research topic in computer vision. Recently, the most successful approaches use dense trajectories that extract a large number of trajectories and features on the trajectories into a codeword. In this paper, we evaluate various features in the framework of dense trajectories on several types of datasets. We implement 13 features in total by including five different types of descriptor, namely motion-, shape-, texture- trajectory- and co-occurrence-based feature descriptors. The experimental results show a relationship between feature descriptors and performance rate at each dataset. Different scenes of traffic, surgery, daily living and sports are used to analyze the feature characteristics. Moreover, we test how much the performance rate of concatenated vectors depends on the type, top-ranked in experiment and all 13 feature descriptors on fine-grained datasets. Feature evaluation is beneficial not only in the activity recognition problem, but also in other domains in spatio-temporal recognition.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    PublisherSpringer Verlag
    Pages634-646
    Number of pages13
    Volume9474
    ISBN (Print)9783319278568
    DOIs
    Publication statusPublished - 2015
    Event11th International Symposium on Advances in Visual Computing, ISVC 2015 - Las Vegas, United States
    Duration: 2015 Dec 142015 Dec 16

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9474
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Other

    Other11th International Symposium on Advances in Visual Computing, ISVC 2015
    CountryUnited States
    CityLas Vegas
    Period15/12/1415/12/16

    ASJC Scopus subject areas

    • Computer Science(all)
    • Theoretical Computer Science

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  • Cite this

    Kataoka, H., Aoki, Y., Iwata, K., & Satoh, Y. (2015). Evaluation of vision-based human activity recognition in dense trajectory framework. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9474, pp. 634-646). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9474). Springer Verlag. https://doi.org/10.1007/978-3-319-27857-5_57