Traditional image stitching algorithms are slow and fail to meet the requirements of obtaining large-resolution panoramic images in real time. To solve these problems, this study proposes an image registration algorithm based on CUDA’s speeded-up-robust features (SURF) and carries out CUDA parallel optimization on the detection and description of feature points of traditional SURF algorithms in terms of GPU thread execution model, programming model, and memory model. In addition, based on FLANN and RANSAC algorithms, the study adopts a bidirectional matching strategy to match features and improve registration accuracy. The experimental results show that compared with serial algorithms, the proposed parallel algorithm can achieve an acceleration ratio of more than 10 times for images with different resolutions, and the registration accuracy is 17% higher than that of traditional registration algorithms, with an optimal accuracy of as high as 96%. Therefore, the SURF algorithm based on CUDA acceleration can be widely used in the field of security monitoring to realize the real-time registration of panoramic images.