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Truck Driver Fatigue Detection Based on Video Sequences in Open-Pit Mines
- Release time:2021-11-15
- Hits:
Impact Factor:
2.4DOI number:
10.3390/math9222908Journal:
MathematicsKey Words:
open-pit truck; driver fatigue; feature coding; LRCNAbstract:
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.Indexed by:
Journal paperDiscipline:
EngineeringFirst-Level Discipline:
Mining EngineeringDocument Type:
JVolume:
9Issue:
22Page Number:
2908Number of Words:
6000Translation or Not:
noDate of Publication:
2021-11-15Included Journals:
SCI