Convergence of Internet, Broadcasting and Communication

Learning Model for Avoiding Drowsy Driving with MoveNet and Dense Neural Network



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.


2.1 Drowsy driving detection through single-image eyes detection
2.2 Drowsy driving detection through consecutive-image head posture and eye detection
3.1 Gathering and Preprocessing the Data
3.2. Extracting Body Key Point Location Data using MoveNet
3.3. Training and evaluating the DNN Classification Model
3.4 Drowsiness Warning Application


  • Jinmo Yang Dept. of Physics, Korea University, Republic of Korea
  • Janghwan Kim Dept. of Software and Communication Engineering, Hongik University, Republic of Korea
  • Young Chul Kim Dept. of Software and Communication Engineering, Hongik University, Republic of Korea
  • Kidu Kim Telecommunications Technology Association, Republic of Korea


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