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A Robust Method for Moving Object Detection Using Modified Statistical Mean Method

机译:改进的统计均值方法的运动目标检测鲁棒方法

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Moving object detection is low-level, important task for any visual surveillance system. One of the aim of this paper is to, to describe various approaches of moving object detection such as background subtraction, temporal difference, as well as pros and cons of these techniques. A statistical mean technique [10] has been used to overcome the problem in previous techniques. Even statistical mean method also suffers with the problem of superfluous effects of foreground objects. In this paper, the presented method tries to overcome this effect as well as reduces the computational complexity up to some extent. In this paper, a robust algorithm for automatic, noise detection and removal from moving objects in video sequences is presented. The algorithm considers static camera parameters
机译:运动对象检测对于任何视觉监视系统都是低级的重要任务。本文的目的之一是描述运动物体检测的各种方法,例如背景减法,时间差异以及这些技术的优缺点。统计均值技术[10]已用于克服先前技术中的问题。甚至统计均值方法也遭受前景对象的多余影响的问题。在本文中,提出的方法试图克服这种影响,并在一定程度上降低计算复杂度。在本文中,提出了一种鲁棒的算法,用于自动,噪声检测和从视频序列中的运动对象中去除。该算法考虑静态相机参数

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