MACHINE LEARNING IS SET TO DETECT DRIVER DROWSINESS TO REDUCE ROAD ACCIDENTS
Source – https://www.analyticsinsight.net/
The machine learning approach is used for drowsiness detection of drivers to reduce the number of road accidents per year. Integration of machine learning algorithms into computer vision can help to detect whether drivers are feeling drowsy through video streams and facial recognition. IIT Ropar has built an algorithm that can extract facial features of drowsiness like eyes and mouths to effectively detect the real-time feeling of a driver. This is expected to reduce road accidents in a country by alerting the drivers on time.
There are three techniques that the team of IIT Ropar developed— driver’s operational behavior can be tracked with the understanding of the steering wheel, accelerator or brake patterns and speed; physiological features of a driver like heart rate, head posture or pulse rate and computer vision system to recognize facial expressions. Machine learning can detect driver’s drowsiness accurately in multiple vehicle models.
The tech companies and institutes have realized the utmost need for machine learning algorithms in drowsiness detection. Scientists have developed this alert system with the help of Video Stream Processing that analyses an eye blink through an Eye Aspect Ratio (EAR) as well as the Euclidean distance of an eye. IoT can send a warning message with a degree of collision along with real-time location data. The Raspberry Pi, OpenCV or Python monitoring system will help in issuing this crucial message on the spot.
EAR includes a simple calculation that is based on the ratio of distances between the lengths and width of the eyes. The eye aspect is very crucial in detecting drowsiness. Thus, EAR can be plotted for multiple frames of a video sequence through computer vision. There are three command lines to order the detector to use— shape-predictor, alarm, and webcam. If the EAR for a driver starts to decline over multiple frames, the machine learning algorithms can detect that the driver is drowsy. There is also a presence of Mouth Aspect Ratio (MAR)— the ratio of distances between the length and width of the mouth of a driver. This will detect when the driver will yawn and lose control over the mouth. There is a significant emphasis on the pupil of the eye known as Pupil Circularity. It helps to detect whether the eyes are half-open or almost closed during driving.
Thus, the advancement in cutting-edge technology is utilized in reducing road accidents per year with the help of machine learning algorithms. It is a natural feeling to be drowsy on roads for numerous causes. Thus, it is the work of machine learning algorithms to protect drivers and their families from incurring a massive loss.