Complexity Reduction by Modified Scale-Space Construction in SIFT Generation Optimized for a Mobile GPU

Abstract

Scale-invariant feature transform (SIFT) is one of the most widely used local features for computer vision in mobile devices. A mobile graphic processing unit (GPU) is often used to run computer-vision applications using SIFT features, but the performance in such a case is not powerful enough to generate SIFT features in real time. This paper proposes an efficient scheme to optimize the SIFT algorithm for a mobile GPU. It analyzes the conventional scale-space construction step in the SIFT generation, finding that reducing the size of the Gaussian filter and the scale-space image leads to a significant speedup with only a slight degradation of the quality of the features. Based on this observation, the SIFT algorithm is modified and implemented for real-time execution. Additional optimization techniques are employed for a further speedup by efficiently utilizing both the CPU and the GPU in a mobile processor. The proposed SIFT generation scheme achieves a processing speed of 28.30 frames/s for an image with a resolution of $1280 \times 720$ running on a Galaxy S5 LTE-A device, thereby gaining a speedup by the factors of 114.78 and 4.53 over CPU- and GPU-only implementations, respectively.