Dynamic Video Stitching via Shakiness Removing
Dynamic Video Stitching via Shakiness Removing
复制标题
通过抖动消除进行动态视频拼接
DOI：
10.1109/tip.2017.2736603
复制
发表时间：
2018-01-01
影响因子：
10.6
通讯作者：
Li, Guiqing
中科院分区：
文献类型：
--
作者：
Nie, Yongwei;Su, Tan;Li, Guiqing
对手持移动的摄像头拍摄的视频进行拼接可以从本质上增强普通用户的娱乐体验。然而，这样的视频通常包含严重的抖动和大视差，这是具有挑战性的缝合。在本文中，我们提出了一种新的方法，视频拼接和稳定的视频捕获的移动的设备。我们的方法的主要组成部分是一个统一的视频拼接和稳定优化，同时计算拼接和稳定，而不是单独进行。通过这种方式，我们可以获得相对于彼此的最佳缝合和稳定结果，而不会对其中之一产生任何偏差。为了使优化具有鲁棒性，我们提出了一种识别输入视频背景以及输入视频共同背景的方法。这允许我们仅在背景区域上应用我们的优化，这是处理大视差问题的关键。由于拼接依赖于输入视频之间的特征匹配，并且不可避免地存在错误匹配，因此，我们提出了一种区分正确和错误匹配的方法，并将错误匹配消除方案和我们的优化封装到一个循环中，以防止优化受到不良特征匹配的影响。我们测试所提出的方法，当走在沿着忙碌街道的智能手机拍摄的视频因果关系，并使用拼接和稳定性分数来评估产生的全景视频定量。不同的例子上的实验表明，我们的结果比（具有挑战性的情况下）或至少与（简单的情况下）以前的方法的结果相当。
Stitching videos captured by hand-held mobile cameras can essentially enhance entertainment experience of ordinary users. However, such videos usually contain heavy shakiness and large parallax, which are challenging to stitch. In this paper, we propose a novel approach of video stitching and stabilization for videos captured by mobile devices. The main component of our method is a unified video stitching and stabilization optimization that computes stitching and stabilization simultaneously rather than does each one individually. In this way, we can obtain the best stitching and stabilization results relative to each other without any bias to one of them. To make the optimization robust, we propose a method to identify background of input videos, and also common background of them. This allows us to apply our optimization on background regions only, which is the key to handle large parallax problem. Since stitching relies on feature matches between input videos, and there inevitably exist false matches, we thus propose a method to distinguish between right and false matches, and encapsulate the false match elimination scheme and our optimization into a loop, to prevent the optimization from being affected by bad feature matches. We test the proposed approach on videos that are causally captured by smartphones when walking along busy streets, and use stitching and stability scores to evaluate the produced panoramic videos quantitatively. Experiments on a diverse of examples show that our results are much better than (challenging cases) or at least on par with (simple cases) the results of previous approaches.