基于均值聚类和几何关系的运动背景估计算法研究

Research on Algorithm of Moving Background Estimation Based on Means Clustering and Triangulation

为了在动态场景图像序列中准确地完成全局运动估计,实现对运动背景的补偿,提出了基于均值聚类和几何关系的运动背景估计算法。首先,利用Harris算法提取两帧图像的特征点,建立特征点匹配对。其次,利用K-means聚类算法去除在匹配过程中存在的明显错误的特征点对。再次,利用三角几何关系去除位于运动目标上的特征点。最后,利用随机样本一致(RANdom SAmple Consensus,RANSAC)算法和最小二乘方法求出运动参数。分析实验结果得出:本文算法比原始算法的峰值信噪比提高了5%左右,所耗时间减少了50ms。实验结果表明:该算法能更加精确的实现运动背景估计,提高了运动背景估计的鲁棒性,同时提高了计算速度。

A new algorithm for moving background estimation based on means clustering and triangulation is proposed to exactly obtain global motion estimation in dynamic scene and to real- ize moving background compensation. Firstly, Harris algorithm is used to extract feature points of two frames and initialize the feature point matching pairs. Secondly, K-means clustering algo- rithm is used to remove the apparent error feature point matching pairs. Thirdly, triangulation is used to remove the feature points in moving target. Lastly, RANdom SAmple Consensus algo- rithm and least square method are used to solve moving parameters. With analysis on the results, it is concluded that the PSNR of our algorithm is about 5 % larger than original algorithm and the time of our algorithm used is 50ms lesser than original algorithm. The results indicate that mov- ing background estimation can be realized more precisely and the robustness of moving back- ground estimation is improved by our algorithm. Furthermore, the computing speed is also raised by the algorithm.