Discontinuous seam cutting for enhanced video stitching

Video stitching requires proper seam cutting technique to decide the boundary of the sub video volume cropped from source videos. In theory, approaches such as 3D graph-cuts that search the entire spatiotemporal volume for a cutting surface should provide the best results. However, given the tremendous data size of the camera array video source, the 3D graph-cuts algorithm is extremely resource-demanding and impractical. In this paper, we propose a sequential seam cutting scheme, which is a dynamic programming algorithm that scans the source videos frame-by-frame, updates the pixels' spatiotemporal constraints, and gradually builds the cutting surface in low space complexity. The proposed scheme features flexible seam finding conditions based on temporal and spatial coherence as well as salience. Experimental results show that by relaxing the seam continuity constraint, the proposed video stitching can better handle abrupt motions or sharp edges in the source, reduce stitching artifacts, and render enhanced visual quality.