On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition

Ramana ORUGANTI, Ibrahim Radwan, Abhinav Dhall, Roland GOECKE

    Research output: A Conference proceeding or a Chapter in BookConference contribution

    4 Citations (Scopus)

    Abstract

    Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. Human behaviour analysis methods can be categorised into three classes based on the type of features. The three representations are local, region of interest and densely sampled based representations. Local feature representation, such as Spatio-Temporal Interest Points (STIP), are quite popular for modelling temporal aspects in human action recognition. Region of Interest (ROI) based feature representations try to capture and represent human body part regions. Densely sampled representations capture information at uniformly spaced intervals spread in space and temporal directions of the given video. In this paper, we investigate the effect of human body part (ROI) information in large scale action recognition. Further, we also investigate the effect of its fusion with Harris 3D points (local representation) information and densely sampled representations. All experiments use a Bag-of-Words framework. We present our results on large class benchmark databases such as the UCF50 and HMDB51 datasets
    Original languageEnglish
    Title of host publicationProceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013)
    EditorsPaulo de Souza, Ulrich Engelke, Ashfaqur Rahman
    Place of PublicationPiscataway, USA
    PublisherIEEE
    Pages1-8
    Number of pages8
    ISBN (Electronic)9781479921263
    DOIs
    Publication statusPublished - 2013
    Event2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Hobart, Hobart, Australia
    Duration: 26 Nov 201328 Nov 2013
    http://staff.itee.uq.edu.au/lovell/aprs/dicta13/ (Conference Webpage)

    Conference

    Conference2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
    Abbreviated titleDICTA 2013
    CountryAustralia
    CityHobart
    Period26/11/1328/11/13
    Internet address

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    Fusion reactions
    Experiments

    Cite this

    ORUGANTI, R., Radwan, I., Dhall, A., & GOECKE, R. (2013). On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition. In P. D. Souza, U. Engelke, & A. Rahman (Eds.), Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013) (pp. 1-8). Piscataway, USA: IEEE. https://doi.org/10.1109/DICTA.2013.6691507
    ORUGANTI, Ramana ; Radwan, Ibrahim ; Dhall, Abhinav ; GOECKE, Roland. / On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition. Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). editor / Paulo de Souza ; Ulrich Engelke ; Ashfaqur Rahman. Piscataway, USA : IEEE, 2013. pp. 1-8
    @inproceedings{18a1854961be47baab80c40ee442fafe,
    title = "On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition",
    abstract = "Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. Human behaviour analysis methods can be categorised into three classes based on the type of features. The three representations are local, region of interest and densely sampled based representations. Local feature representation, such as Spatio-Temporal Interest Points (STIP), are quite popular for modelling temporal aspects in human action recognition. Region of Interest (ROI) based feature representations try to capture and represent human body part regions. Densely sampled representations capture information at uniformly spaced intervals spread in space and temporal directions of the given video. In this paper, we investigate the effect of human body part (ROI) information in large scale action recognition. Further, we also investigate the effect of its fusion with Harris 3D points (local representation) information and densely sampled representations. All experiments use a Bag-of-Words framework. We present our results on large class benchmark databases such as the UCF50 and HMDB51 datasets",
    keywords = "Human action recognition, Behaviour recognition, Large Scale",
    author = "Ramana ORUGANTI and Ibrahim Radwan and Abhinav Dhall and Roland GOECKE",
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    ORUGANTI, R, Radwan, I, Dhall, A & GOECKE, R 2013, On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition. in PD Souza, U Engelke & A Rahman (eds), Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). IEEE, Piscataway, USA, pp. 1-8, 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA), Hobart, Australia, 26/11/13. https://doi.org/10.1109/DICTA.2013.6691507

    On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition. / ORUGANTI, Ramana; Radwan, Ibrahim; Dhall, Abhinav; GOECKE, Roland.

    Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). ed. / Paulo de Souza; Ulrich Engelke; Ashfaqur Rahman. Piscataway, USA : IEEE, 2013. p. 1-8.

    Research output: A Conference proceeding or a Chapter in BookConference contribution

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    AU - Radwan, Ibrahim

    AU - Dhall, Abhinav

    AU - GOECKE, Roland

    PY - 2013

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    N2 - Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. Human behaviour analysis methods can be categorised into three classes based on the type of features. The three representations are local, region of interest and densely sampled based representations. Local feature representation, such as Spatio-Temporal Interest Points (STIP), are quite popular for modelling temporal aspects in human action recognition. Region of Interest (ROI) based feature representations try to capture and represent human body part regions. Densely sampled representations capture information at uniformly spaced intervals spread in space and temporal directions of the given video. In this paper, we investigate the effect of human body part (ROI) information in large scale action recognition. Further, we also investigate the effect of its fusion with Harris 3D points (local representation) information and densely sampled representations. All experiments use a Bag-of-Words framework. We present our results on large class benchmark databases such as the UCF50 and HMDB51 datasets

    AB - Automatic analysis of human behaviour in large collections of videos is gaining interest, even more so with the advent of file sharing sites such as YouTube. Human behaviour analysis methods can be categorised into three classes based on the type of features. The three representations are local, region of interest and densely sampled based representations. Local feature representation, such as Spatio-Temporal Interest Points (STIP), are quite popular for modelling temporal aspects in human action recognition. Region of Interest (ROI) based feature representations try to capture and represent human body part regions. Densely sampled representations capture information at uniformly spaced intervals spread in space and temporal directions of the given video. In this paper, we investigate the effect of human body part (ROI) information in large scale action recognition. Further, we also investigate the effect of its fusion with Harris 3D points (local representation) information and densely sampled representations. All experiments use a Bag-of-Words framework. We present our results on large class benchmark databases such as the UCF50 and HMDB51 datasets

    KW - Human action recognition

    KW - Behaviour recognition

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    ORUGANTI R, Radwan I, Dhall A, GOECKE R. On the Effect of Human Body Parts in Large Scale Human Behaviour Recognition. In Souza PD, Engelke U, Rahman A, editors, Proceedings of the 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA 2013). Piscataway, USA: IEEE. 2013. p. 1-8 https://doi.org/10.1109/DICTA.2013.6691507