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Moving target tracking algorithm based on the confidence measure of motion vectors

机译:基于运动向量置信度量的移动目标跟踪算法

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Change detection using a difference picture has been used to detect the location of moving targets and to track them. This method needs the assumption of a static camera, and global motion compensation is required in the case of a moving camera. We suggest a method for finding minimum bounding rectangles (MBRs) of moving targets in the image sequences using moving region detection, especially with a moving camera. If the global motion parameter is inaccurately estimated, the estimated locations of targets will not be accurate either. To alleviate this problem, we introduce the concept of the confidence measure and achieve more accurate estimation of global motion. With a moving camera, the new global motion estimation algorithm performs more precisely and it reduces the background compensation errors for change detection. Experimental results show that the proposed method successfully removes the background region and extracts MBRs of the targets.
机译:使用差异图片的变化检测已被用于检测移动目标的位置并跟踪它们。该方法需要假设静态相机,并且在移动相机的情况下需要全局运动补偿。我们建议使用移动区域检测来查找图像序列中移动目标的最小边界矩形(MBR)的方法,尤其是移动相机。如果估计全局运动参数,则目标的估计位置也不准确。为了减轻这个问题,我们介绍了置信度量的概念,实现了对全球运动的更准确估算。通过移动相机,新的全局运动估计算法更精确地执行,并且降低了改变检测的背景补偿误差。实验结果表明,该方法成功地去除了背景区域并提取了目标的MBR。

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