Abstract
In recent years, graphic processing units (GPUs) have emerged as an attractive alternative to CPUs for implementing algorithms in a wide range of applications. The focus of this work is to give an overview about the current state on using GPUs for computer vision. We describe briefly tools like CUDA, OpenCL and OpenACC used for GPU programming and their respective advantages / disadvantages. We give information about the current state of the art for implementing important computer vision algorithms like optical flow, KLT feature point tracking and SIFT descriptor extraction efficiently on the GPU. Finally, we describe open source frameworks which either provide GPU-accelerated computer vision algorithms or which are helpful for porting algorithms to the GPU.
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