Performance of a simple remote video-based eye tracker with GPU acceleration

Abstract

Eye tracking is a well-established tool that is often utilised in research. There are currently many different types of eye trackers available, but they are either expensive, or provide a relatively low sampling frequency. The eye tracker presented in this paper was developed in an effort to address the lack of low-cost high-speed eye trackers. It utilises the Graphical Processing Unit (GPU) in an attempt to parallelise aspects of the process to localize feature points in eye images to attain higher sampling frequencies. Moreover, the proposed implementation allows for the system to be used on a variety of different GPUs. The developed solution is capable of sampling at frequencies of 200 Hz and higher, while allowing for head movements within an area of 10×6×10 cm and an average accuracy of one degree of visual angle. The entire system can be built for less than 700 euros, and will run on a mid-range laptop.