Incremental stitching for real-time video processing: a comprehensive performance study

Video stitching aims to integrate multiple videos with limited fields of view into a panoramic video with a wider FoV. This paper presents a comprehensive performance study on incremental stitching for real-time video processing. Traditional feature-based methods, such as SIFT and ORB, often struggle with sparse feature points or matching errors in complex scenes, leading to misalignment and ghosting artifacts. The paper reviews current research hotspots, including improving stitching robustness in complex scenes, enhancing real-time performance, exploring unsupervised or self-supervised learning methods, and optimizing algorithm efficiency. It highlights the differences between image and video stitching, emphasizing the additional requirement for temporal consistency in video stitching. The paper also discusses challenges faced by unsupervised learning in video stitching, such as feature alignment, dynamic scene handling, temporal stability, model generalization, and computational efficiency. Despite advancements, current frameworks still struggle with geometric distortions, computational inefficiency for high-resolution videos, and temporal inconsistency in long sequences. In conclusion, the field of video stitching continues to evolve, with potential future innovations driven by advancements in deep learning, computational efficiency, and robust handling of parallax and temporal consistency.