Design and Implementation of Real-time Algorithms for Eye Tracking and PERCLOS Measurement for on board Estimation of Alertness of Drivers

The alertness level of drivers can be estimated with the use of computer vision based methods. The level of fatigue can be found from the value of PERCLOS. It is the ratio of closed eye frames to the total frames processed. The main objective of the thesis is the design and implementation of real-time algorithms for measurement of PERCLOS. In this work we have developed a real-time system which is able to process the video onboard and to alarm the driver in case the driver is in alert. For accurate estimation of PERCLOS the frame rate should be greater than 4 and accuracy should be greater than 90%. For eye detection we have used mainly two approaches Haar classifier based method and Principal Component Analysis (PCA) based method for day time. During night time active Near Infra Red (NIR) illumination is used. Local Binary Pattern (LBP) histogram based method is used for the detection of eyes at night time. The accuracy rate of the algorithms was found to be more than 90% at frame rates more than 5 fps which was suitable for the application.