REAL TIME SLEEP / DROWSINESS DETECTION - Project Report
Intermediate Python Project - Driver Drowsiness Detection System with OpenCV & Keras - DataFlair
Abstract — Machine learning techniques have been used in order to predict the condition and emotion of a driver to provide information that will improve safety on the road. It is an application of artificial intelligence. Artificial Intelligence is a method by which systems can automatically learn as well as improve without being explicitly programmed. A drivers condition can be estimated by bio-indicators, behavior while driving as well as the expressions on the face of a driver. In this paper we present an all-inclusive survey of recent works related to driver drowsiness detection and alert system.
Driver Drowsiness Monitoring System using Visual Behaviour and Machine Learning
PQDT Open is getting a new home! Please refer to this FAQ. Driver drowsiness has become a significant threat resulting in traffic accidents and causing severe injuries or death. Therefore, monitoring this condition is paramount to alert drivers, and to avoid fatal accidents. A hardware design to detect the drowsiness is proposed in this thesis and the outputs to justify the thesis is simulated in LT Spice.
Monitoring the individuals action while driving by examining the manoeuvred of the vehicle can be considered a very prominent process in order to improve safety while travelling. To differentiate between unintentional and intentional car steering wheel inputs, will be the primary key aspect to be discovered, such as a rapid large steering source could show the driver's degree of alertness. Almost all the figures have identified driver drowsiness as a high priority vehicle safeness concern.