一种新的全局运动估计算法

全局运动估计是计算机视觉、视频处理等领域广泛采用的手段之一。该文提出一种直接利用压缩视频码流进行全局运动估计的新算法。就运动模型而言,采用了复杂性与准确性较好均衡的六参数仿射模型。为了提高估计精度和计算效率,文章首先提出一种新的视频背景前景分割方法,基于分割的结果,采用背景宏块的运动矢量进行全局运动参数估计。然后,根据参数估计误差的统计特性,一部分运动矢量会当作局外的样值而被剔除,利用剩余的运动矢量中重新估计参数可以提高估计精度。实验验证了提出的全局运动估计算法的计算效率和精度。

A New Global Motion Estimation Algorithm

GME(Global Motion Estimation) is an important tool widely used in computer vision,video processing,and other fields.In this paper,we propose a new method for the estimation of global motion from compressed image sequences.With regard to global motion models,we adopt six-parameter affine model because of its reasonable tradeoff between complexity and accuracy.In order to improve accuracy and computational efficiency of global motion estimation,we present a new algorithm for segmentation between background and foreground.Then,motion vectors samples associated with background macroblocks are selected to estimate motion model parameters.Lastly,according to the statistics of estimated error,some sample pairs may be rejected as outliers and the parameters may be refined by estimating from the remaining data.The extensive experiments show that the proposed method is efficient and robust in terms of both computational complexity and accuracy.