Pedestrian near-miss analysis on vehicle-mounted driving recorders

Teppei Suzuki, Yoshimitsu Aoki, Hirokatsu Kataoka

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

    3 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Title of host publicationProceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages416-419
    Number of pages4
    ISBN (Electronic)9784901122160
    DOIs
    Publication statusPublished - 2017 Jul 19
    Event15th IAPR International Conference on Machine Vision Applications, MVA 2017 - Nagoya, Japan
    Duration: 2017 May 82017 May 12

    Other

    Other15th IAPR International Conference on Machine Vision Applications, MVA 2017
    CountryJapan
    CityNagoya
    Period17/5/817/5/12

    ASJC Scopus subject areas

    • Computer Science Applications
    • Computer Vision and Pattern Recognition

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  • Cite this

    Suzuki, T., Aoki, Y., & Kataoka, H. (2017). Pedestrian near-miss analysis on vehicle-mounted driving recorders. In 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