Department of Computer Science, National Chiao Tung University, Hsinchu.
IEEE Trans Vis Comput Graph. 2013 Aug;19(8):1354-61. doi: 10.1109/TVCG.2013.11.
Properly handling parallax is important for video stabilization. Existing methods that achieve the aim require either 3D reconstruction or long feature trajectories to enforce the subspace or epipolar geometry constraints. In this paper, we present a robust and efficient technique that works on general videos. It achieves high-quality camera motion on videos where 3D reconstruction is difficult or long feature trajectories are not available. We represent each trajectory as a Bézier curve and maintain the spatial relations between trajectories by preserving the original offsets of neighboring curves. Our technique formulates stabilization as a spatial-temporal optimization problem that finds smooth feature trajectories and avoids visual distortion. The Bézier representation enables strong smoothness of each feature trajectory and reduces the number of variables in the optimization problem. We also stabilize videos in a streaming fashion to achieve scalability. The experiments show that our technique achieves high-quality camera motion on a variety of challenging videos that are difficult for existing methods.
正确处理视差对于视频稳定化非常重要。现有的实现这一目标的方法需要 3D 重建或长特征轨迹来强制子空间或极线几何约束。在本文中，我们提出了一种在通用视频上工作的鲁棒且高效的技术。它可以在 3D 重建困难或没有长特征轨迹的视频上实现高质量的相机运动。我们将每个轨迹表示为贝塞尔曲线，并通过保留相邻曲线的原始偏移量来保持轨迹之间的空间关系。我们的技术将稳定化表述为一个时空优化问题，该问题找到平滑的特征轨迹并避免视觉失真。贝塞尔表示允许每个特征轨迹具有很强的平滑性，并减少了优化问题中的变量数量。我们还以流的方式稳定视频以实现可扩展性。实验表明，我们的技术可以在各种具有挑战性的视频上实现高质量的相机运动，这些视频对于现有方法来说很难处理。
