Eye Tracking Algorithm Based on Multi Model Kalman Filter

One of the most important pieces of Human Machine Interface (HMI) equipment is an eye tracking system that is used for many different applications. This paper aims to present an algorithm in order to improve the efficiency of eye tracking in the image by means of a multi-model Kalman filter. In the classical Kalman filter, one model is used for estimation of the object, but in the multi-model Kalman filter, several models are used for estimating the object. The important features of the multiple-model Kalman filter are improving the efficiency and reducing its estimating errors relative to the classical Kalman filter. The proposed algorithm consists of two parts. The first step is recognizing the initial position of the eye, and Support Vector Machine (SVM) has been used in this part. In the second part, the position of the eye is predicted in the next frame by using a multi-model Kalman filter, which applies constant speed and acceleration models based on the normal human eye. Doi: 10.28991/HIJ-2022-03-01-02 Full Text: PD