Gaze Tracking with Particle Swarm Optimization

• Main Authors: Lee, Chia-Yi, 李家宜
• Other Authors: Kao, Wen-Chung
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/24049735188194612938

碩士 === 國立臺灣師範大學 === 電機工程學系 === 104 === The eye tracking system is a new human machine interface device which can analyze the gaze path by tracking the eyeball movement. It has become increasingly popular in the consumer market, due to the fact that the recorded gaze tracking results can be applied to the study of human attention span, cognitive psychology and in the fields of neuroscience, psychology, education, as well as consumer products. However, gaze tracking system mostly rely on infra-ray (IR) light to enhance the image quality of the eyeball, making the application environments and scenarios of the gaze tracking system an issue to be solved. As a result, more and more researchers devoted themselves to the development of visible light eye tracking system, but very few reliable systems achieve high accuracy. The proposed system aims to improve the system accuracy by modifying the eyeball matching algorithm, even when the images are taken in poor lighting conditions. The improvements come from the modifications of image preprocessing, searching algorithm with the particle swarm optimization (PSO), and a new calibration method. The experimental results indicate the system error is less than 1.12 degree and the entire system reaches processing speed of 30 frames/s.