Convolutional three-stream network fusion for driver fatigue detection from infrared videos

Xiaoxi Ma, Lap Pui Chau, Kim Hui Yap, Guiju Ping

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

6 Citations (Scopus)

Abstract

We propose a convolutional three-stream network architecture for driver fatigue detection from infrared videos that are available both in the daytime and in the night time. Specifically, the convolutional three-stream network architecture incorporates current-infrared-frame-based spatial information, optical-flows-based short-term temporal information of two consecutive infrared frames and optical flow-motion history image-based (OF-MHI-based) temporal information within the infrared video sequence. And then these three networks are fused at the last convolutional layer by 3D CNN. Besides, an estimation method to evaluate the current driver fatigue level is proposed based on the fatigue detection results from previous frames, which helps to generate alerts properly in real-life driving applications. We show that the proposed method achieves state-of-the-art performance, 94.68% accuracy, in our driver behavior dataset using the infrared data.

Original languageEnglish
Title of host publication2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103976
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019 - Sapporo, Japan
Duration: 26 May 201929 May 2019

Publication series

NameProceedings - IEEE International Symposium on Circuits and Systems
Volume2019-May
ISSN (Print)0271-4310

Conference

Conference2019 IEEE International Symposium on Circuits and Systems, ISCAS 2019
Country/TerritoryJapan
CitySapporo
Period26/05/1929/05/19

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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