Video stitching in surveillance systems

The goal is to explore techniques such as image correspondence using interest points, robust matching with improved RANSAC, homography, and background subtraction and blending. The basic idea of stitch several images into a panorama is to map all the images onto a reference plane. In this project, we choose frame as the reference plane and the homography matrices between other frame images and reference frame are computed using SIFT and improved RANSAC algorithms. Identify key points and matches using SIFT. Then the key point correspondences between two frames are filtered out by the default threshold of descriptor matching. First choose correspondences from the matches, and implemented Normalized Direct Linear Transformation (DLT) to estimate the homography matrix. This process is then automated by improved RANSAC that is iterated, randomly choosing 4 correspondences each time. The degree of match is evaluated by calculating the error of other correspondences based on such homography. The best the homography matrix is then found with most inliers. By using improved RANSAC algorithm. Once the projection transform updated in real time, we still need to blend the frames to compensate for exposure differences and other misalignments.