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Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
| Content Provider | MDPI |
|---|---|
| Author | Wang, Yi He, Zhengxiang Wang, Liguan |
| Copyright Year | 2021 |
| 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. |
| Starting Page | 2908 |
| e-ISSN | 22277390 |
| DOI | 10.3390/math9222908 |
| Journal | Mathematics |
| Issue Number | 22 |
| Volume Number | 9 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-11-15 |
| Access Restriction | Open |
| Subject Keyword | Mathematics Transportation Science and Technology Open-pit Truck Driver Fatigue Feature Coding Lrcn |
| Content Type | Text |
| Resource Type | Article |
