Driver Drowsiness Detection using Raspberry Pi - Potluri, Tejaswi
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Preventing road accidents caused by drowsy drivers is crucial for road safety. To achieve this, drowsiness detection while driving is necessary. The detection process involves monitoring the driver's eye movements, head position, and facial expressions. The latest advancements in computer vision and machine learning enable the detection of drowsiness signs in real time. Our goal is to create a drowsiness detection system that alerts drivers before it's too late for an accident to occur. This system can be incorporated into vehicles, personal devices, providing a successful solution for…mehr

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Produktbeschreibung
Preventing road accidents caused by drowsy drivers is crucial for road safety. To achieve this, drowsiness detection while driving is necessary. The detection process involves monitoring the driver's eye movements, head position, and facial expressions. The latest advancements in computer vision and machine learning enable the detection of drowsiness signs in real time. Our goal is to create a drowsiness detection system that alerts drivers before it's too late for an accident to occur. This system can be incorporated into vehicles, personal devices, providing a successful solution for reducing the threat of drowsy driving. In conclusion, the proposed driver drowsiness detection system using Raspberry Pi provides a reliable and cost-effective solution for enhancing road safety. By detecting and alerting the driver to their drowsiness, the system can prevent accidents caused by drowsy driving, reducing the risk of injuries and fatalities on the road.
Autorenporträt
Dr. Tejaswi Potluri ist eine gute Forscherin im Bereich Computer Vision. Sie hat 10 Veröffentlichungen in renommierten Fachzeitschriften wie Springer, Elsevier, Scopus und Web of Science. Sie hat viele Sitzungen in renommierten Hochschulen über Computer Vision, Videoverarbeitung und Lehr-Lern-Praktiken gehalten.