Pedestrian near-miss analysis on vehicle-mounted driving recorders

Teppei Suzuki, Yoshimitsu Aoki, Hirokatsu Kataoka

    研究成果: Conference contribution

    2 引用 (Scopus)

    抜粋

    Recently, a demand for video analysis on vehicle-mounted driving recorders has been increasing in vision-based safety systems, such as for autonomous vehicles. The technology must be positioned one of the most important task, however, the conventional traffic datasets (e.g. KITTI, Caltech Pedestrian) are not included any dangerous scenes (near-miss scenes), even though the objective of a safety system is to avoid danger. In this paper, (i) we create a pedestrian near-miss dataset on vehicle-mounted driving recorders and (ii) propose a method to jointly learns to predict pedestrian detection and its danger level {high, low, no-danger} with convolutional neural networks (CNN) based on the ResNets. According to the result, we demonstrate the effectiveness of our approach that achieved 68% accuracy of joint pedestrian detection and danger label prediction, and 58.6fps processing time on the self-collected pedestrian near-miss dataset.

    元の言語English
    ホスト出版物のタイトルProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
    出版者Institute of Electrical and Electronics Engineers Inc.
    ページ416-419
    ページ数4
    ISBN(電子版)9784901122160
    DOI
    出版物ステータスPublished - 2017 7 19
    イベント15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
    継続期間: 2017 5 82017 5 12

    Other

    Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
    Japan
    Nagoya
    期間17/5/817/5/12

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computer Vision and Pattern Recognition

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  • これを引用

    Suzuki, T., Aoki, Y., & Kataoka, H. (2017). Pedestrian near-miss analysis on vehicle-mounted driving recorders. : Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017 (pp. 416-419). [7986889] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/MVA.2017.7986889