proposed a method for face detection by using landmark points and track the face to find fatigue drivers.
Real action pose collection 01 pdf driver#
Facial feature-based methods can evaluate the target in real-time without invasive instruments, and they are inexpensive and more accessible than other methods.ĭriver behaviors can be extracted to detect driver drowsiness. These approaches need to consider invasive captors, which can affect driving negatively.
Real action pose collection 01 pdf skin#
The most critical physiological signals that used and investigated are ElectroEncephaloGram (EEG), ElectroOculoGram (EOG), activities of the autonomous nervous system from ElectroCardioGram (ECG), Skin Temperature (ST), Galvanic Skin Response (GSR) and also intramuscular activities as ElectroMyoGram (EMG). Several studies have been done during the last years based on these methods. Signal-based methods infer drowsiness from psychophysiological parameters. However, these approaches are too slow for real-time tasks. Vehicle-based methods try to infer drowsiness from vehicle situation and monitor the variations of steering wheel angle, acceleration, lateral position, etc.
There are three main categories of drowsiness detectors: Vehicle-based, Signal-based, and Facial feature-based. It also improves the performance of the Advanced Driver Assistance Systems (ADAS) and Driver Monitoring System (DMS) as a result, road safety.
Therefore, the detection of driver drowsiness could be a suitable methodology to prevent accidents. The reports demonstrate that the first cause of such crashes are drivers. According to published reports from the World Health Organization (WHO), traffic accidents are one of the top 10 causes that lead to death in the world.
0 kommentar(er)
