Jiachen Liang
ABSTRACT
With the rapid economic development and the remote dispatch of materials during the epidemic, transportation is gradually "burnout", which increases the probability of traffic accidents. The main cause of traffic accidents is fatigue driving behavior, so it is extremely necessary to design a fatigue driving detection and early warning system. We can combine face recognition technology with driver's facial features, and embed the 68 feature point detection model of the face generated based on the residual neural network training into this system via transfer learning. This model can be used to extract the real-time state of eyes, mouth and head and transfer it to the Opencv library. By using the EAR-Perclos, MAR-Perclos, Pnp-Hpe, Pitch-Perclos algorithms, we can calculate the fatigue value of the feature point data collected by the Opencv library, extract EAR, MAR, and Pitch changes within 10s to set the fatigue threshold. The real-time data is then compared with the threshold value. If it exceeds the threshold value range, fatigue tendency or fatigue driving activity can be determined. The experimental verification of the system is carried out through the video of fatigue state and non-fatigue state, which confirms the algorithm accuracy in fatigue driving detection.
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Index Terms
- Fatigue driving detection system based on Perclos algorithm optimization
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