Abstract: Aiming at the complex and low real-time defects of the SIFT feature extraction algorithm, a real-time SIFT algorithm based on GPU is proposed, called CoSift (CUDA Optimized SIFT).Firstly, the algorithm uses the CUDA stream concurrency mechanism to construct the SIFT scale space.In this process, the high-speed memory in the CUDA memory model is fully utilized to improve data access speed, and the two-dimensional Gaussian convolution kernel is optimized to reduce the amount of computation.Then, the warp-based histogram policy is designed to rebalance the workload during the characterization process.Compared with the traditional algorithm of the CPU version and the improved algorithm of the GPU version, the proposed algorithm greatly improves the real-time performance of the SIFT algorithm without reducing the accuracy of feature extraction, and has a relatively higher optimization effect on large-size images.CoSift can extract features within 7.7~8.8ms (116.28~129.87fps) on the GTX1080Ti.The algorithm effectively reduces the complexity of the traditional SIFT algorithm process, improves the real-time performance, and is convenient to be applied in scenarios where the real-time requirement of SIFT algorithm is higher.

Key words: CUDA, Feature extraction, Parallel acceleration, Real-time, Scale invariant feature transformation