Joint Video Stitching and Stabilization From Moving Cameras

Abstract

In this paper, we extend image stitching to video stitching for videos that are captured for the same scene simultaneously by multiple moving cameras. In practice, videos captured under this circumstance often appear shaky. Directly applying image stitching methods for shaking videos often suffers from strong spatial and temporal artifacts. To solve this problem, we propose a unified framework in which video stitching and stabilization are performed jointly. Specifically, our system takes several overlapping videos as inputs. We estimate both inter motions (between different videos) and intra motions (between neighboring frames within a video). Then, we solve an optimal virtual 2D camera path from all original paths. An enlarged field of view along the virtual path is finally obtained by a space-temporal optimization that takes both inter and intra motions into consideration. Two important components of this optimization are that (1) a grid-based tracking method is designed for an improved robustness, which produces features that are distributed evenly within and across multiple views, and (2) a mesh-based motion model is adopted for the handling of the scene parallax. Some experimental results are provided to demonstrate the effectiveness of our approach on various consumer-level videos and a Plugin, named "Video Stitcher" is developed at Adobe After Effects CC2015 to show the processed videos.

References

  • Joint Video Stitching and Stabilization from Moving Cameras.
    Heng Guo, Tong He, Shuaicheng Liu, Bing Zeng, Moncef Gabbouj. IEEE Transactions on Image Processing (TIP), 2016. [PDF]