Human interaction prediction using deep temporal features

    Research output: Chapter in Book/Conference paperConference paper

    13 Citations (Scopus)

    Abstract

    Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.

    Original languageEnglish
    Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    EditorsGang Hua, Herve Jegou
    PublisherSpringer-Verlag London Ltd.
    Pages403-414
    Number of pages12
    Volume9914 LNCS
    ISBN (Print)9783319488806
    DOIs
    Publication statusPublished - 2016
    Event14th European Conference on Computer Vision, ECCV 2016 - Amsterdam, Netherlands
    Duration: 8 Oct 201616 Oct 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9914 LNCS
    ISSN (Print)03029743
    ISSN (Electronic)16113349

    Conference

    Conference14th European Conference on Computer Vision, ECCV 2016
    CountryNetherlands
    CityAmsterdam
    Period8/10/1616/10/16

    Fingerprint

    Image coding
    Prediction
    Interaction
    Image Coding
    Network architecture
    Robots
    Neural networks
    Network Architecture
    Robot
    Human
    Neural Networks
    Motion
    Range of data

    Cite this

    Ke, Q., Bennamoun, M., An, S., Boussaid, F., & Sohel, F. (2016). Human interaction prediction using deep temporal features. In G. Hua, & H. Jegou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9914 LNCS, pp. 403-414). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS). Springer-Verlag London Ltd.. https://doi.org/10.1007/978-3-319-48881-3_28
    Ke, Qiuhong ; Bennamoun, Mohammed ; An, Senjian ; Boussaid, Farid ; Sohel, Ferdous. / Human interaction prediction using deep temporal features. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Gang Hua ; Herve Jegou. Vol. 9914 LNCS Springer-Verlag London Ltd., 2016. pp. 403-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
    @inproceedings{3dae47c26dce4f2e890e7285218c2f43,
    title = "Human interaction prediction using deep temporal features",
    abstract = "Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.",
    keywords = "CNN, Interaction prediction, Temporal convolution",
    author = "Qiuhong Ke and Mohammed Bennamoun and Senjian An and Farid Boussaid and Ferdous Sohel",
    year = "2016",
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    editor = "Gang Hua and Herve Jegou",
    booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
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    }

    Ke, Q, Bennamoun, M, An, S, Boussaid, F & Sohel, F 2016, Human interaction prediction using deep temporal features. in G Hua & H Jegou (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 9914 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9914 LNCS, Springer-Verlag London Ltd., pp. 403-414, 14th European Conference on Computer Vision, ECCV 2016, Amsterdam, Netherlands, 8/10/16. https://doi.org/10.1007/978-3-319-48881-3_28

    Human interaction prediction using deep temporal features. / Ke, Qiuhong; Bennamoun, Mohammed; An, Senjian; Boussaid, Farid; Sohel, Ferdous.

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Gang Hua; Herve Jegou. Vol. 9914 LNCS Springer-Verlag London Ltd., 2016. p. 403-414 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9914 LNCS).

    Research output: Chapter in Book/Conference paperConference paper

    TY - GEN

    T1 - Human interaction prediction using deep temporal features

    AU - Ke, Qiuhong

    AU - Bennamoun, Mohammed

    AU - An, Senjian

    AU - Boussaid, Farid

    AU - Sohel, Ferdous

    PY - 2016

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    N2 - Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.

    AB - Interaction prediction has a wide range of applications such as robot controlling and prevention of dangerous events. In this paper, we introduce a new method to capture deep temporal information in videos for human interaction prediction. We propose to use flow coding images to represent the low-level motion information in videos and extract deep temporal features using a deep convolutional neural network architecture. We tested our method on the UT-Interaction dataset and the challenging TV human interaction dataset, and demonstrated the advantages of the proposed deep temporal features based on flow coding images. The proposed method, though using only the temporal information, outperforms the state of the art methods for human interaction prediction.

    KW - CNN

    KW - Interaction prediction

    KW - Temporal convolution

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    U2 - 10.1007/978-3-319-48881-3_28

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    T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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    BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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    Ke Q, Bennamoun M, An S, Boussaid F, Sohel F. Human interaction prediction using deep temporal features. In Hua G, Jegou H, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 9914 LNCS. Springer-Verlag London Ltd. 2016. p. 403-414. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-48881-3_28