Machine Learning is Set to Detect Driver Drowsiness to Reduce Road Accidents

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.