Automatic mosaic of surveillance images based on SIFT feature matching

In this paper, an improved Scale Invariant Feature Transform (SIFT) feature-based approach for efficient automatic mosaic surveillance images was proposed. In the image registration step, SIFT Feature was extracted efficiently and reliable matching was carried out, and Random Sample Consensus (RANSAC) was improved to guarantee stability and decide the transformation parameters of the images stitching. In the image fuse step, a radiometric and color compensation stage allows the development of an automatic and seamless mosaicing system. The results show that our approach performs well, even in processing the surveillance images with low overlapping area, moving objects, shape differences and noise.