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Precise Foreground Detection Algorithm Using Motion Estimation, Minima and Maxima Inside the Foreground Object

机译:使用前景对象内的运动估计,最小值和最大值的精确前景检测算法

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In this paper the precise foreground mask is obtained in a complex environment by applying simple and effective methods on a video sequence consisting of multi-colour and multiple foreground object environment. To detect moving objects we use a simple algorithm based on block-based motion estimation, which requires less computational time. To obtain a full and improved mask of the moving object, we use an opening-and-closing-by-reconstruction mechanism to identify the minima and maxima inside the foreground object by applying a set of morphological operations. This further enhances the outlines of foreground objects at various stages of image processing. Therefore, the algorithm does not require the knowledge of the background image. That is why it can be used in real world video sequences to detect the foreground in cases where we do not have a background model in advance. The comparative performance results demonstrate the effectiveness of the proposed algorithm.
机译:在本文中,通过对包含多色和多个前景对象环境的视频序列应用简单有效的方法,可以在复杂的环境中获得精确的前景蒙版。为了检测运动物体,我们使用基于基于块的运动估计的简单算法,该算法需要较少的计算时间。为了获得完整且经过改进的运动对象蒙版,我们使用了一种通过重构的打开和关闭机制,通过应用一组形态学运算来识别前景对象内部的最小值和最大值。这进一步增强了图像处理各个阶段中前景对象的轮廓。因此,该算法不需要了解背景图像。这就是为什么在我们事先没有背景模型的情况下,可以在现实世界的视频序列中使用它来检测前景的原因。对比性能结果证明了该算法的有效性。

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