A New Unsupervised Deep Learning Algorithm for Fine-Grained Detection of Driver Distraction-Reference-Cited by-同舟云学术

A New Unsupervised Deep Learning Algorithm for Fine-Grained Detection of Driver Distraction

Published:2022-10 Issue:10 Volume:23 Page:19272-19284
ISSN:1524-9050
Container-title:IEEE Transactions on Intelligent Transportation Systems
language:
Short-container-title:IEEE Trans. Intell. Transport. Syst.

Author:

Li Bing¹,Chen Jie¹^ORCID,Huang Zhixiang¹^ORCID,Wang Haitao¹,Lv Jianming¹,Xi Jingmin¹,Zhang Jun²^ORCID,Wu Zhongcheng²

Affiliation:

1. Information Materials and Intelligent Sensing Laboratory of Anhui Province, and the Key Laboratory of Intelligent Computing and Signal Processing, Ministry of Education, Anhui University, Hefei, China

2. Hefei Institute of Physical Science, Chinese Academy of Sciences, Hefei, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Anhui Province

China Postdoctoral Science Foundation

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Science Applications,Mechanical Engineering,Automotive Engineering

Link

http://xplorestaging.ieee.org/ielx7/6979/9916643/09759226.pdf?arnumber=9759226

Reference62 articles.

1. Distracted driver detection based on a CNN with decreasing filter size;qin;IEEE Trans Intell Transp Syst,2021

2. Towards Computationally Efficient and Realtime Distracted Driver Detection With MobileVGG Network

3. Going deeper with convolutions

4. ImageNet: A large-scale hierarchical image database

5. Fully Convolutional Networks for Semantic Segmentation

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