Drowsiness_Detection_IOT_Final_Project

This project successfully performs drowsiness detection and is run fully on a Raspberry Pi. The most prevalent use case for drowsiness detection is in the context of driver drowsiness detection. The drowsiness detection is performed via video capture, image processing, and key eye landmark identification. OpenCV and Python’s dlib library are used in order to conduct image processing and apply machine learning based facial feature recognition, respectively. When the driver closes their eyes for five consecutive seconds, the program triggers an alert. The video footage of the driver is broadcast to a web page (primarily for testing purposes), and an alert message is displayed over the video footage when drowsiness is detected. Future development of the project would consist of connecting a bluetooth audio device to the raspberry pi (such as the car audio system) which can successfully wake up the driver. Additionally, the implementation of this project into everyday use would not require the web page component, as it is necessary for testing but not for later usage.