Drowsiness Detection System in Real Time Based on Behavioral Characteristics of Driver using Machine Learning Approach
Keywords:Drowsiness detection, Real-time detection, OpenCV, Dlib, Facial landmark detection, Driver safety, Machine Learning
The process of determining if a person, generally a driver, is becoming sleepy or drowsy while performing a task such as driving is known as drowsiness detection. It is a necessary system for detecting and alerting drivers to their tiredness, which might impair their driving ability and lead to accidents. The project aims to create a reliable and efficient system capable of real-time detection of drowsiness using OpenCV, Dlib, and facial landmark detection technologies. The project's results show that the sleepiness detection method can accurately and precisely identify tiredness in real time. The technology is less intrusive and more economical than conventional sleepiness detection techniques. The system is based on a 68 facial landmark detector, which is a highly trained and effective detector capable of recognizing human face points. The detector aids in assessing whether the driver's eyes are closed or open. The system analyses the data collected by the detector using machine learning methods to discover patterns associated with drowsiness. When drowsiness is detected, the system incorporates a warning mechanism, such as an alarm or a vibration in the steering wheel, to notify the driver. A variety of studies with different drivers and driving conditions were used to evaluate the performance of the real-time driver drowsiness detection system. The results show that the technology can detect tiredness properly and deliver timely warnings to the driver. This method can assist in preventing drowsy driving incidents, enhancing road safety, and saving lives. The results indicated that the algorithm had an average accuracy rate of 94% for identifying tiredness in drivers.
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Copyright (c) 2023 Gauri Adarsh, Vineet Singh, Dr Shikha Singh, Dr Bramah Hazela
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