A Novel Fatigue Driving Detection Method Under the Mask-Wearing Condition
23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021
; : 1022-1028, 2022.
Article
in English
| Scopus | ID: covidwho-1909205
ABSTRACT
Fatigue driving is one of the major contributors to road accidents. Nowadays, COVID-19 is reaching epidemic proportions, which directly leads to the phenomenon of mask-wearing becomes ordinary among drivers. Most of the existing fatigue detection systems are unable to effectively determine the factual fatigue status of a driver that wearing a mask. Therefore, we propose a quick-witted fatigue detection system to counteract the obstruction of masks. The system detects faces by means of a pyramidbox-based approach. Then a modified PFLD-based method will predict the facial landmarks, from which the eye aspect ratio (EAR) is calculated. Ultimately, our self-made FDUM dataset was tested by using the evaluation method that combined PERCLOS and a method for blink frequency based on Gaussian distribution. Our system can achieve 97.06% accuracy in determining the fatigue status of the driver under the mask, which represents an excellent recognition rate of the system. © 2021 IEEE.
Full text:
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Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
23rd IEEE International Conference on High Performance Computing and Communications, 7th IEEE International Conference on Data Science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021
Year:
2022
Document Type:
Article
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