Driver Drowsiness Alert System (DDAS) using computer vision and Haar Classifier
README for Driver Drowsiness Alert System (DDAS) using computer vision and Haar Classifier
Overview: The Driver Drowsiness Alert System (DDAS) is a computer vision-based solution that uses Haar Classifier to detect driver drowsiness accurately. The system monitors the geometric features of the driver's eyes and mouth and gives voice alerts through an eSpeak module when the driver is feeling drowsy or yawning. This system aims to reduce the number of accidents caused by driver drowsiness and prevent the destructive outcomes resulting from fatigue-related negligence.
Installation: To use the DDAS, you will need to install the OpenCV library, Python. You can install OpenCV using pip using the following command:
sudo apt-get install espeak
Usage: To use the DDAS, simply run the 'python final-integration.py' file using Python. The system will then start detecting the driver's drowsiness based on the geometric features of the eyes and mouth. If the driver is feeling drowsy or yawning, the system will give voice alerts through the eSpeak module.
Testing: We have extensively tested the DDAS to ensure its reliability and accuracy. The system has met or exceeded industry standards for driver drowsiness detection. However, we recommend testing the system in a controlled environment to ensure optimal performance.
Contributing: We welcome contributions to the DDAS project. If you have any ideas or suggestions to improve the system's performance, feel free to create a pull request or open an issue on the GitHub repository.
Conclusion: The Driver Drowsiness Alert System (DDAS) is an effective and affordable solution to prevent accidents caused by driver drowsiness. By utilizing computer vision and Haar Classifier, the system accurately detects the driver's level of drowsiness and gives voice alerts to prevent accidents. We hope this project will contribute to society's well-being by enhancing transportation safety and reducing the number of fatalities and injuries resulting from road accidents.
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