Gabbouj Moncef
IEEE Trans Image Process. 2016 Nov;25(11):5491-5503. doi: 10.1109/TIP.2016.2607419. Epub 2016 Sep 8.
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.
在本文中,我们将图像拼接扩展到视频拼接,用于多个移动摄像头同时为同一场景拍摄的视频。在实际应用中,在这种情况下拍摄的视频往往会出现抖动。直接将图像拼接方法应用于抖动视频通常会产生强烈的空间和时间伪影。为了解决这个问题,我们提出了一个统一的框架,在其中联合执行视频拼接和稳定化。具体而言,我们的系统将几个重叠的视频作为输入。我们估计帧间运动(不同视频之间)和帧内运动(视频内相邻帧之间)。然后,我们从所有原始路径中求解出一条最优的虚拟二维摄像机路径。最后,通过同时考虑帧间和帧内运动的时空优化,沿着虚拟路径获得一个扩大的视野。该优化的两个重要组成部分是:1)设计了一种基于网格的跟踪方法以提高鲁棒性,该方法生成在多个视图内和跨多个视图均匀分布的特征;2)采用基于网格的运动模型来处理场景视差。提供了一些实验结果来证明我们的方法在各种消费级视频上的有效性,并在Adobe After Effects CC2015中开发了一个名为“Video Stitcher”的插件来展示处理后的视频。