CUDA Implementation For Eye Location On Infrared Images

Abstract

Parallel programming using GPUs is a modern solution to reduce computation time for large tasks. This is done by dividing algorithms in smaller parts which can be executed simultaneously. CUDA has many practical applications especially in video processing, medical imaging and machine learning. This paper presents how parallel implementations can speedup a ground truth data generation algorithm for eye location on infrared driver recordings which is executed on a database with more than 2 million frames. Computation time is much shorter compared to a sequential CPU implementation which makes it feasible to run it multiple times if updates are required and even use it in real time applications.