Presentation 2021-03-05
An approach for predicting traffic accidents at intersections with 360 degree panorama images
Daiki Tanaka, Kiyoharu Aizawa,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) In this study, we used deep learning to predict traffic accidents. Traffic accidents are caused by a complex combination of various factors, and it is physically difficult to collect detailed data for each location. We investigated a new problem setting of determining whether a traffic accident occurs in the future using only a single 360 degree image of each location. Experimental results demonstrate that a deep neural network can predict traffic accident locations with an accuracy of more than 78%.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) 360 degree panorama images / Deep learning / Traffic accident prediction
Paper # PRMU2020-97
Date of Issue 2021-02-25 (PRMU)

Conference Information
Committee PRMU / IPSJ-CVIM
Conference Date 2021/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English) Computer Vision and Pattern Recognition for specific environment
Chair Yoichi Sato(Univ. of Tokyo)
Vice Chair Akisato Kimura(NTT) / Masakazu Iwamura(Osaka Pref. Univ.)
Secretary Akisato Kimura(Mobility Technologies) / Masakazu Iwamura(Chubu Univ.)
Assistant Takashi Shibata(NTT) / Masashi Nishiyama(Tottori Univ.)

Paper Information
Registration To Technical Committee on Pattern Recognition and Media Understanding / Special Interest Group on Computer Vision and Image Media
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) An approach for predicting traffic accidents at intersections with 360 degree panorama images
Sub Title (in English)
Keyword(1) 360 degree panorama images
Keyword(2) Deep learning
Keyword(3) Traffic accident prediction
1st Author's Name Daiki Tanaka
1st Author's Affiliation The University of Tokyo(The Univ. of Tokyo)
2nd Author's Name Kiyoharu Aizawa
2nd Author's Affiliation The University of Tokyo(The Univ. of Tokyo)
Date 2021-03-05
Paper # PRMU2020-97
Volume (vol) vol.120
Number (no) PRMU-409
Page pp.pp.158-163(PRMU),
#Pages 6
Date of Issue 2021-02-25 (PRMU)