Speeding up SIFT using GPU

Scale Invariant Feature Transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping and large scale image retrieval. Although SIFT descriptors are highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real time response, and in algorithms dealing with very large scale databases. This paper presents a parallel implementation of SIFT on a GPU, where we obtain a speed of around 55 fps for a 640×480 image. One of the main contributions of our work is the novel combined kernel optimization that has led to a significant improvement of 21.79% in the execution speed. We compare our results with the existing implementations in the literature that accelerate SIFT, and find that our implementation has better speedup than the most of them.