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Spatially and Temporally Optimized Video Stabilization

机译:时空优化的视频稳定

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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.
机译:正确处理视差对于视频稳定至关重要。现有的达到目标的方法需要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,我们提出了一种适用于一般视频的强大有效的技术。它可以在难以进行3D重建或无法使用长特征轨迹的视频上实现高质量的摄像机运动。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公式化为时空优化问题,可以找到平滑的特征轨迹并避免视觉失真。贝塞尔(Bézier)表示使每个特征轨迹具有很强的平滑度,并减少了优化问题中的变量数量。我们还以流媒体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摄像机运动,而这些视频对于现有方法来说是困难的。

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