【摘要】 在视频序列的全局运动估计中,前景运动对象的存在常常会大幅度地降低估计的精度,为此提出一种对前景对象自适应的高精度全局运动估计算法.该算法以像素块为单位,利用块内外点的比重判定前景区域,同时引入马尔可夫聚类方法进行后处理,有效地提高了运动对象的定位精度;通过对目标函数引入权重系数增强对残差的鲁棒性,以进一步提高算法的估计精度.此外,基于像素掩模的3层金字塔构建序列图像,并将改进的梯度方法引入到优化过程中,提高了算法的实时性.对不同运动类型的标准视频序列的实验结果表明,该算法有效地提高了全局运动估计的精度和速度.更多还原

【Abstract】 The accuracy of global motion estimation (GME) is usually compromised by the presence of foreground moving objects. This paper presents a GME algorithm with high estimation accuracy. A three-stage mask-based pyramid is built to decrease computational complexity; the initial translation is robustly estimated by a feedback-based algorithm. An approach based on the ratio of outliers in a block and Markov clustering is developed to locate the motion object more effectively than the histogram-based or block-based method; a weighted quadratic error function is adopted instead of the regular truncated quadratic function to increase the robustness to residual error; an improved gradient-based algorithm is introduced to speed up the convergence. From the experimental results on standard video sequences with different motion style,we conclude that the proposed method could achieve significant performance gains for standard and self-photographed video sequences with camera translation.更多还原