The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
A Survey on State-of-the-Art Drowsiness Detection Techniques
Open accessAbstract
Drowsiness or fatigue is a major cause of road accidents and has significant implications for road safety. Several deadly accidents can be prevented if the drowsy drivers are warned in time. A variety of drowsiness detection methods exist that monitor the drivers' drowsiness state while driving and alarm the drivers if they are not concentrating on driving. The relevant features can be extracted from facial expressions such as yawning, eye closure, and head movements for inferring the level of drowsiness. The biological condition of the drivers' body, as well as vehicle behavior, is analyzed for driver drowsiness detection. This paper presents a comprehensive analysis of the existing methods of driver drowsiness detection and presents a detailed analysis of widely used classification techniques in this regard. First, in this paper, we classify the existing techniques into three categories: behavioral, vehicular, and physiological parameters-based techniques. Second, top supervised learning techniques used for drowsiness detection are reviewed. Third, the pros and cons and comparative study of the diverse method are discussed. In addition, the research frameworks are elaborated in diagrams for better understanding. In the end, overall research findings based on the extensive survey are concluded which will help young researchers for finding potential future work in the relevant field.
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Cites
Eyeblink-based Anti-Spoofing in Face Recognition from a Generic Webcamera
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Driver fatigue detection based on eye tracking and dynamic template matching
Horng, W.-B., Chen, C.-Y., Chang, Y., & Fan, C.-H. (2004) · IEEE International Conference on Networking, Sensing and Control, 2004 · doi:10.1109/icnsc.2004.1297400 · Cited by 229
Online Detection of Driver Fatigue Using Steering Wheel Angles for Real Driving Conditions
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Drowsy driver detection using representation learning
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Driver Fatigue Detection Based on Eye State Recognition
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Self-citationPrivacy-Preserving Federated Transfer Learning for Driver Drowsiness Detection
Zhang, L., Saito, H., Yang, L., & Wu, J. (2022) · IEEE Access · doi:10.1109/access.2022.3192454 · Cited by 54
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Metadata
- Authors
- Ramzan, M., Khan, H. U., Awan, S. M., Ismail, A., Ilyas, M., & Mahmood, A.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 61904-61919
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2943512776
- Cited by
- 361
- References
- 22
- FWCI
- 40.56
- Type
- journal article
- Language
- en
- Open access
- gold
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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