Hierarchical deep neural networks to detect driver drowsiness

Driver drowsiness is one of the main reasons for deadly accidents, especially on suburban roads. Researchers have used many methods for analyzing videos and detecting drowsiness, and the most up-to-date methods among them are using deep learning. This paper proposes a hierarchical framework comprisi...

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Bibliographic Details
Published in:Multimedia tools and applications 2021-04, Vol.80 (10), p.16045-16058
Main Authors: Jamshidi, Samaneh, Azmi, Reza, Sharghi, Mehran, Soryani, Mohsen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Artificial neural networks
Computer Communication Networks
Computer Science
Data Structures and Information Theory
Deep learning
Driver fatigue
Lighting
Machine learning
Methods
Mouth
Multimedia
Multimedia Information Systems
Neural networks
Researchers
Roads & highways
Special Purpose and Application-Based Systems
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Summary:Driver drowsiness is one of the main reasons for deadly accidents, especially on suburban roads. Researchers have used many methods for analyzing videos and detecting drowsiness, and the most up-to-date methods among them are using deep learning. This paper proposes a hierarchical framework comprising deep networks with split spatial and temporal phases referred to as hierarchical deep drowsiness detection (HDDD) network. The proposed method uses ResNet to detect the driver’s face, lighting condition, and whether the driver is wearing glasses or not. This phase also causes a significant increase in eyes and mouth detection percentage in the next stage. Afterward, the LSTM network is used to take advantage of temporal information between the frames. The average accuracy of the drowsiness detection system is reached 87.19 percent.
ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-021-10542-7