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

Yi Wang, Zhengxiang He and Liguan Wang
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Yi Wang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Zhengxiang He: School of Resources and Safety Engineering, Central South University, Changsha 410083, China
Liguan Wang: School of Resources and Safety Engineering, Central South University, Changsha 410083, China

Mathematics, 2021, vol. 9, issue 22, 1-14

Abstract: 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.

Keywords: open-pit truck; driver fatigue; feature coding; LRCN (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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