• Complex
  • Title
  • Keyword
  • Abstract
  • Scholars
  • Journal
  • ISSN
  • Conference
搜索

Author:

Deng, Wanghua (Deng, Wanghua.) | Zhan, Zeyan (Zhan, Zeyan.) | Yu, Yi (Yu, Yi.) | Wang, Weizeng (Wang, Weizeng.)

Indexed by:

EI

Abstract:

In recent years, the number of car has been increasing, and car has gradually become an indispensable mean of transportation for people to travel. However, along with the rapid growth of car brings convenience to people's life, it also brings many negative effects to the development of road traffic. More and more traffic accidents have happened, and most traffic accidents are caused by fatigue driving. In order to reduce traffic accidents caused by fatigue driving, many methods have been proposed. However, these methods cannot guarantee the accuracy and speed of detection at the same time. So, a fatigue driving detection method based on multi feature fusion is presented in this paper. Firstly, MTCNN is used to improve the face tracking algorithm based on MedianFlow. Then a new face key points detection model based on CNN is proposed, the result of face key points detection can be used to locate the eyes. Finally, information such as eye closing time, blinking frequency and head position are fused to detect fatigue driving. Experimental results show that the fatigue driving detection method proposed in this paper has a good result on speed and accuracy. © 2019 IEEE.

Keyword:

Accidents Image processing Feature extraction Fatigue of materials

Author Community:

  • [ 1 ] [Deng, Wanghua]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 2 ] [Zhan, Zeyan]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 3 ] [Yu, Yi]Beijing University of Technology, Beijing Engineering Research Center for IoT Software and Systems, Beijing, China
  • [ 4 ] [Wang, Weizeng]Beijing University of Technology, Beijing, China

Reprint Author's Address:

Email:

Show more details

Related Keywords:

Related Article:

Source :

Year: 2019

Page: 407-411

Language: English

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 10

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

Online/Total:590/10713917
Address:BJUT Library(100 Pingleyuan,Chaoyang District,Beijing 100124, China Post Code:100124) Contact Us:010-67392185
Copyright:BJUT Library Technical Support:Beijing Aegean Software Co., Ltd.