An effective segmentation of moving objects by a novel local regions-based level set

M. Boumehed, B. Alshaqaqi, A. Ouamri, M. Keche, Mohamed El Amine Ouis
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Abstract

This paper presents new local regions based level set model for segmenting multiple moving objects in video sequences captured by a stationary camera. The main idea evolves around the reformulation of well-known global energy in local way, by utilizing little squared windows centered on each point over a thin band surrounding the zero level set, hence the object contour can be reshaped into small local interior and exterior regions that lead to compute a family of adaptive local energies. Moreover, we propose to adapt the smoothness of the contours with an automatic stopping criterion. The proposed method has been tested on different real videos, and the experiment results demonstrate that our algorithm can segment effectively and accurately the moving objects.
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一种新的基于局部区域的水平集对运动目标的有效分割
本文提出了一种新的基于局部区域的水平集模型,用于分割静止摄像机拍摄的视频序列中的多个运动目标。其主要思想是围绕着众所周知的全局能量在局部的重新制定,通过在零水平集周围的薄带上利用以每个点为中心的小正方形窗口,从而将物体轮廓重塑为小的局部内部和外部区域,从而计算一系列自适应的局部能量。此外,我们还提出了一种自动停止准则来适应轮廓的平滑度。在不同的真实视频中对该方法进行了测试,实验结果表明,该算法可以有效、准确地分割运动目标。
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