Wu Rouwan, Xu Zhiyong, Zhang Jianlin, Zhang Lihong
Key Laboratory of Optical Engineering, Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China.
Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610200, China.
Sensors (Basel). 2021 Apr 3;21(7):2505. doi: 10.3390/s21072505.
Obtaining accurate global motion is a crucial step for video stabilization. This paper proposes a robust and simple method to implement global motion estimation. We don't extend the framework of 2D video stabilization but add a "plug and play" module to motion estimation based on feature points. Firstly, simple linear iterative clustering (SLIC) pre-segmentation is used to obtain superpixels of the video frame, clustering is performed according to the superpixel centroid motion vector and cluster center with large value is eliminated. Secondly, in order to obtain accurate global motion estimation, an improved K-means clustering is proposed. We match the feature points of the remaining superpixels between two adjacent frames, establish a feature points' motion vector space, and use improved K-means clustering for clustering. Finally, the richest cluster is being retained, and the global motion is obtained by homography transformation. Our proposed method has been verified on different types of videos and has efficient performance than traditional approaches. The stabilization video has an average improvement of 0.24 in the structural similarity index than the original video and 0.1 higher than the traditional method.
获得准确的全局运动是视频稳定的关键步骤。本文提出了一种鲁棒且简单的方法来实现全局运动估计。我们没有扩展二维视频稳定的框架，而是在基于特征点的运动估计中添加了一个“即插即用”模块。首先，使用简单线性迭代聚类（SLIC）预分割来获取视频帧的超像素，根据超像素质心运动向量进行聚类，并消除具有较大值的聚类中心。其次，为了获得准确的全局运动估计，提出了一种改进的K均值聚类。我们匹配两个相邻帧之间剩余超像素的特征点，建立特征点的运动向量空间，并使用改进的K均值聚类进行聚类。最后，保留最丰富的聚类，并通过单应性变换获得全局运动。我们提出的方法已在不同类型的视频上得到验证，并且比传统方法具有更高的性能。稳定后的视频在结构相似性指数上比原始视频平均提高了0.24，比传统方法高0.1。
