Road safety is a major concern; however, a great extent of the accidents is caused due to driver fatigue. Under this approach, an effective system to detect sleepy driving is sought to structure. This type of system that delves into fatigue indicators including head pose, facial expressions, and eye tracking to identify drowsiness precisely in real time utilizes methodologies such as machine learning and computer vision. The first goal is to provide early warnings which will trigger the driver and prevent potential crashes. It is from this design that drivers and other people who control virtual devices get easy, immediate integration into vehicles and personal electronic devices as it considerably diminishes drowsy driving risks. The choice of Raspberry Pi technology used at the base of this solution is also due to the combination of performance, adaptability, and cost-efficiency, thereby providing stability and general accessibility for virtually all multipurpose vehicles. Video capture and face detection are accomplished mainly by OpenCV and Haar cascade technologies. Warnings to alert drivers when their fatigue level is still at the tolerable, acceptable level is the pivotal role our technology plays in enhancing road safety by preventing accidents caused by driver fatigue and this ultimately helps to create safer driving conditions. Our system, supported by Raspberry Pi technology stands prepared as an essential tool in combating drowsy driving - making substantial contributions towards ensuring road safety and enhancing the well-being of drivers.
Raspberry Pi-Based Driver Drowsiness Detection
09.02.2024
507548 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
IEEE | 2020
|Springer Verlag | 2017
|Overview of Research on Driver Drowsiness Definition and Driver Drowsiness Detection
British Library Conference Proceedings | 1994
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