Depth video-based two-stream convolutional neural networks for driver fatigue detection

Xiaoxi Ma, Lap Pui Chau, Kim Hui Yap

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

17 Citations (Scopus)

Abstract

Recently, much research efforts have been dedicated to the development of computer-vision-based driver fatigue detection systems. Most of them utilize the RGB data, and focus on driver status detection during the day. However, drivers are more likely to be tired and drowsy during night time. In this paper, we present a driver fatigue detection system based on CNN using depth video sequences, which helps to provide alerts properly to fatigue drivers during the night time. Specifically, the two-stream CNN architecture incorporates spatial information of current depth frame and temporal information of neighboring depth frames which is represented by motion vectors. Besides, we propose a background removal system for depth video sequence of driving. Our method is trained and evaluated on our driver behavior dataset. Experiments show that the accuracy of the proposed method achieves 91.57%, which outperforms the baseline system within the recent state-of-the-art.

Original languageEnglish
Title of host publicationProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
EditorsLei Wang, Minghui Dong, Yanfeng Lu, Haizhou Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages155-158
Number of pages4
ISBN (Electronic)9781538632758
DOIs
Publication statusPublished - 10 Apr 2018
Externally publishedYes
Event5th International Conference on Orange Technologies, ICOT 2017 - Singapore, Singapore
Duration: 8 Dec 201710 Dec 2017

Publication series

NameProceedings of the 2017 International Conference on Orange Technologies, ICOT 2017
Volume2018-January

Conference

Conference5th International Conference on Orange Technologies, ICOT 2017
Country/TerritorySingapore
CitySingapore
Period8/12/1710/12/17

Keywords

  • Action recognition
  • Depth videos
  • Driver fatigue detection
  • Two-stream CNN

ASJC Scopus subject areas

  • Health Informatics
  • Instrumentation
  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Health(social science)

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