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Pre-Activated 3D CNN and Feature Pyramid Network for Traffic Accident Detection

Authors
Kim, HyunwooPark, SeokmokPaik, Joonki
Issue Date
Jan-2020
Publisher
IEEE
Keywords
C3D; traffic accident; interval input
Citation
2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE), v.2020-Janua, pp 369 - 371
Pages
3
Journal Title
2020 IEEE INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS (ICCE)
Volume
2020-Janua
Start Page
369
End Page
371
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44024
DOI
10.1109/ICCE46568.2020.9043125
ISSN
0747-668X
Abstract
In this paper, we present a novel traffic accident detection method using a spatio-temporal three-dimensional (3D) convolutional neural network. The proposed method consists of pre-activation ResNet and feature pyramid network (FPN) structure. To reduce computational load with preserving the detection accuracy, we propose interval input method. Experimental results show that the proposed network outperforms existing methods in the sense of both precision and recall measures.
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Paik, Joon Ki
첨단영상대학원 (영상학과)
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