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Authors: Takuya Goto ; Fumihiko Sakaue and Jun Sato

Affiliation: Nagoya Institute of Technology, Nagoya 466-8555, Japan

Keyword(s): Traffic Accident Prediction, Accident Risk, Risk Visualization, Instance Segmentation, Lane Detection.

Abstract: In this paper, we propose a method for visualizing the risk of car accidents in in-vehicle camera images by using deep learning. Our network predicts the future risk of car accidents and generates a risk map image that represents the degree of accident risk at each point in the image. For training our network, we need pairs of in-vehicle images and risk map images, but such datasets do not exist and are very difficult to create. In this research, we derive a method for computing the degree of the future risk of car accidents at each point in the image and use it for constructing the training dataset. By using the dataset, our network learns to generate risk map images from in-vehicle images. The efficiency of our method is tested by using real car accident images.

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Paper citation in several formats:
Goto, T.; Sakaue, F. and Sato, J. (2023). Seeing Risk of Accident from In-Vehicle Cameras. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 672-679. DOI: 10.5220/0011743900003417

@conference{visapp23,
author={Takuya Goto. and Fumihiko Sakaue. and Jun Sato.},
title={Seeing Risk of Accident from In-Vehicle Cameras},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP},
year={2023},
pages={672-679},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011743900003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - Volume 4: VISAPP
TI - Seeing Risk of Accident from In-Vehicle Cameras
SN - 978-989-758-634-7
IS - 2184-4321
AU - Goto, T.
AU - Sakaue, F.
AU - Sato, J.
PY - 2023
SP - 672
EP - 679
DO - 10.5220/0011743900003417
PB - SciTePress

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