Seeing Risk of Accident from In-Vehicle Cameras

Takuya Goto, Fumihiko Sakaue, Jun Sato

2023

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 672-679. DOI: 10.5220/0011743900003417

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Goto T.
AU - Sakaue F.
AU - Sato J.
PY - 2023
SP - 672
EP - 679
DO - 10.5220/0011743900003417
PB - SciTePress