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A Hybrid Background Subtraction Method with Background and Foreground Candidates Detection

机译：具有背景和前景候选检测的混合背景减法

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摘要

Background subtraction for motion detection is often used in video surveillance systems. However, difficulties in bootstrapping restrict its development. This article proposes a novel hybrid background subtraction technique to solve this problem. For performance improvement of background subtraction, the proposed technique not only quickly initializes the background model but also eliminates unnecessary regions containing only background pixels in the object detection process. Furthermore, an embodiment based on the proposed technique is also presented. Experimental results verify that the proposed technique allows for reduced execution time as well as improvement of performance as evaluated by Recall, Precision, F1, and Similarity metrics when used with state-of-the-art background subtraction methods.
机译：用于运动检测的背景减法通常用于视频监视系统中。但是，引导过程中的困难限制了它的发展。本文提出了一种新颖的混合背景扣除技术来解决此问题。为了改善背景扣除的性能，所提出的技术不仅快速初始化背景模型，而且消除了在对象检测过程中仅包含背景像素的不必要区域。此外，还提出了基于所提出的技术的实施例。实验结果证明，与最新的背景扣除方法配合使用时，所提出的技术可减少执行时间并提高性能，如通过查全率，精确度，F1和相似性指标进行评估。

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