A deep neural framework for real-time vehicular accident detection based on motion temporal templates-Reference-Cited by-同舟云学术

A deep neural framework for real-time vehicular accident detection based on motion temporal templates

Published:2022-11 Issue:11 Volume:8 Page:e11397
ISSN:2405-8440
Container-title:Heliyon
language:en
Short-container-title:Heliyon

Author:

Bakheet Samy^ORCID,Al-Hamadi Ayoub^ORCID

Funder

Bundesministerium für Bildung und Forschung

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference38 articles.

1. Violence, injury prevention, and world health organization;World Health Organization,2018

2. Real-time automatic traffic accident recognition using HOF;Sadek,2010

3. Explaining the road accident risk: weather effects;Bergel-Hayat;Accid. Anal. Prev.,2013

4. Study of freeway traffic near an off-ramp;Cassidy;Transp. Res., Part A, Policy Pract.,2002

5. Hybrid models toward traffic detector data treatment and data fusion;Wen,2005

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Residual Network (ResNet) Based Deep Learning Method for Detection and Classification of Accidents in Surveillance Scenes;2023 International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE);2023-11-01

2. Detection of Vehicle Density Based on YoloV7 with Images from Cusco, Peru;2023 3rd International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME);2023-07-19

3. Accident Detection from Drone Footage;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06

4. Car crash detection using ensemble deep learning;Multimedia Tools and Applications;2023-06-30