Deep learning techniques allow us to learn about a person's behavior based on pictures and videos. Using digital cameras, the system can identify and classify a person's behavior based on images and videos. This paper aims to present a method for detecting drivers' drowsiness based on deep ...
The Driver Drowsiness Detection System is designed to enhance road safety by using machine learning to alert drivers when they show signs of drowsiness or distraction. It is developed using Keras, a deep learning framework, and integrates with an in-car camera to continuously monitor the driver's...
Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K. & Messaoudi, A. Detection and prediction of driver drowsiness for the prevention of road accidents using deep neural networks techniques. In Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A. & Khamlichi, Y. (eds.)WITS ...
arXiv:.09498 State Farm Corporate (2016) State farm distracted driver detection. In: Kaggle.com competition website. https://www.kaggle.com/c/state-farm-distracted-driver-detection. Accessed 30 May 2020 Download references Acknowledgements This work was funded by the National Plan for Science, ...
This project helps to detect the drowsiness of the driver.
drowsiness-dataset yawn-eye-dataset-new Tags GPU Language Python Table of Contents Drowsiness Detection System Collaborators NithishM2410 (Owner) dk_here (Editor) License This Notebook has been released under the Apache 2.0 open source license. Continue exploring Input4 files arrow_right_alt Output...
Driver drowsiness detection using face expression recognition. In Proceedings of the IEEE International Conference on Signal and Image Processing Applications, Kuala Lumpur, Malaysia, 16–18 November 2011; pp. 337–341. [Google Scholar] Zhao, K.; Chu, W.S.; Zhang, H. Deep Region and Multi-...
Explore and run machine learning code with Kaggle Notebooks | Using data from Driver Drowsiness Dataset (DDD)
The study in [38] proposed a system for driver drowsiness detection based on integrating deep learning frameworks with IoT devices. The system comprised three phases: eye region detection, eye status detection, and classification. If the driver was drowsy, the alert system was used to notify him...
[8] proposed a method of real-time driver’s drowsiness detection system. The researchers recorded a video through a webcam (Sony CMU-BR300) and detected the driver’s faces using image processing techniques. The researchers used a Support Vector Machine (SVM)-based classification. However, ...