Learning Model for Avoiding Drowsy Driving with MoveNet and Dense Neural NetworkLearning Model for Avoiding Drowsy Driving with MoveNet and Dense Neural Network
- Other Titles
- Learning Model for Avoiding Drowsy Driving with MoveNet and Dense Neural Network
- Authors
- 양진모; 김장환; 김영철; 김기두
- Issue Date
- Nov-2023
- Publisher
- 한국인터넷방송통신학회
- Keywords
- artificial intelligence; drowsiness; posture; single-image detection
- Citation
- The International Journal of Internet, Broadcasting and Communication, v.15, no.4, pp 142 - 148
- Pages
- 7
- Journal Title
- The International Journal of Internet, Broadcasting and Communication
- Volume
- 15
- Number
- 4
- Start Page
- 142
- End Page
- 148
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32363
- DOI
- 10.7236/IJIBC.2023.15.4.142
- ISSN
- 2288-4920
2288-4939
- Abstract
- In Modern days, Self-driving for modern people is an absolute necessity for transportation and many other reasons. Additionally, after the outbreak of COVID-19, driving by oneself is preferred over other means of transportation for the prevention of infection. However, due to the constant exposure to stressful situations and chronic fatigue one experiences from the work or the traffic to and from it, modern drivers often drive under drowsiness which can lead to serious accidents and fatality. To address this problem, we propose a drowsy driving prevention learning model which detects a driver’s state of drowsiness. Furthermore, a method to sound a warning message after drowsiness detection is also presented. This is to use MoveNet to quickly and accurately extract the keypoints of the body of the driver and Dense Neural Network(DNN) to train on real-time driving behaviors, which then immediately warns if an abnormal drowsy posture is detected. With this method, we expect reduction in traffic accident and enhancement in overall traffic safety.
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Collections - Graduate School > Software and Communications Engineering > 1. Journal Articles
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