Summary of Improving Automatic Detection Of Driver Fatigue and Distraction Using Machine Learning, by Dongjiang Wu
Improving automatic detection of driver fatigue and distraction using machine learning
by Dongjiang Wu
First submitted to arxiv on: 4 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents techniques for simultaneously detecting driving fatigue and distracted driving behaviors using vision-based and machine learning-based approaches. The authors propose facial alignment networks to identify facial feature points and detect eye and mouth opening, as well as a MobileNet-based convolutional neural network (CNN) to identify various distracted driving behaviors. Experiments are conducted on a PC-based setup with a webcam, using public datasets and custom datasets created for training and testing. The proposed approach outperforms previous methods in terms of accuracy and computation time. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps create safer roads by monitoring driving behavior in intelligent vehicles. It shows how to use computer vision and machine learning to detect when drivers are tired or distracted. This can help prevent accidents caused by driver fatigue and distracted driving. The authors train their system using real-world data and compare it to other methods, showing that it is more accurate and efficient. |
Keywords
* Artificial intelligence * Alignment * Cnn * Machine learning * Neural network
