Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three s...

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Autores principales:	Yi Wang, Zhengxiang He, Liguan Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	open-pit truck driver fatigue feature coding LRCN Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/8450bfad78424d848bd5e0695d3da140
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id	oai:doaj.org-article:8450bfad78424d848bd5e0695d3da140
record_format	dspace
spelling	oai:doaj.org-article:8450bfad78424d848bd5e0695d3da1402021-11-25T18:17:05ZTruck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines10.3390/math92229082227-7390https://doaj.org/article/8450bfad78424d848bd5e0695d3da1402021-11-01T00:00:00Zhttps://www.mdpi.com/2227-7390/9/22/2908https://doaj.org/toc/2227-7390Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face; (2) extracting and coding the features; (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.Yi WangZhengxiang HeLiguan WangMDPI AGarticleopen-pit truckdriver fatiguefeature codingLRCNMathematicsQA1-939ENMathematics, Vol 9, Iss 2908, p 2908 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	open-pit truck driver fatigue feature coding LRCN Mathematics QA1-939
spellingShingle	open-pit truck driver fatigue feature coding LRCN Mathematics QA1-939 Yi Wang Zhengxiang He Liguan Wang Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
description	Due to complex background interference and weak space–time connection, traditional driver fatigue detection methods perform poorly for open-pit truck drivers. For these issues, this paper presents a driver fatigue detection method based on Libfacedetection and an LRCN. The method consists of three stages: (1) using a face detection module with a tracking method to quickly extract the ROI of the face; (2) extracting and coding the features; (3) combining the coding model to build a spatiotemporal classification network. The innovation of the method is to utilize the spatiotemporal features of the image sequence to build a spatiotemporal classification model suitable for this task. Meanwhile, a tracking method is added to the face detection stage to reduce time expenditure. As a result, the average speed with the tracking method for face detection on video is increased by 74% in comparison with the one without the tracking method. Our best model adopts a DHLSTM and feature-level frame aggregation, which achieves high accuracy of 99.30% on the self-built dataset.
format	article
author	Yi Wang Zhengxiang He Liguan Wang
author_facet	Yi Wang Zhengxiang He Liguan Wang
author_sort	Yi Wang
title	Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
title_short	Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
title_full	Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
title_fullStr	Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
title_full_unstemmed	Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
title_sort	truck driver fatigue detection based on video sequences in open-pit mines
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/8450bfad78424d848bd5e0695d3da140
work_keys_str_mv	AT yiwang truckdriverfatiguedetectionbasedonvideosequencesinopenpitmines AT zhengxianghe truckdriverfatiguedetectionbasedonvideosequencesinopenpitmines AT liguanwang truckdriverfatiguedetectionbasedonvideosequencesinopenpitmines
_version_	1718411392399179776

Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines

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