ABSTRACT The goal of motion segmentation is to segregate a visual scene into independently moving objects. It is an indispensable pre-processing step for various tasks in computer vision and has evolved as an active and flourishing research area in the last few decades. In the sequences captured using a monocular camera, motion segmentation is typically performed by analyzing apparent motion of pixels in the image plane, i.e. the optical flow. Optical flow is generally contemplated as an appropriate representation of image motion. Numerous techniques for reliable flow estimation and subsequent advancements in their framework have been proposed in the last couple of decades and are outlined briefly in this work. The paper attempts to give a summary of diverse optical flow-based approaches used for robust segmentation of static or dynamic scenes containing rigidly moving objects and discusses in brief the shortcomings associated with them.

中文翻译：

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基于光流的运动分割方法概述

摘要 运动分割的目标是将视觉场景分成独立运动的对象。它是计算机视觉中各种任务不可或缺的预处理步骤，并且在过去几十年中发展成为一个活跃且蓬勃发展的研究领域。在使用单目相机捕获的序列中，通常通过分析图像平面中像素的表观运动（即光流）来执行运动分割。光流通常被认为是图像运动的适当表示。在过去的几十年中，已经提出了许多用于可靠流量估计的技术及其框架的后续改进，并在这项工作中进行了简要概述。