运动分割:一种协同方法

C. Fermüller, T. Brodský, Y. Aloimonos
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引用次数: 8

摘要

由于摄像机运动的估计需要独立运动的知识,而运动目标的检测和定位需要摄像机运动的知识,所以运动估计和分割这两个问题需要协同解决。本文提供了一种同时处理这两个问题的方法。这里介绍的技术是基于一个新的概念,“场景粗犷度”,它参数化了估计的场景深度随底层三维运动误差的变化。这个想法是,不正确的3D运动估计会导致估计深度图的扭曲,结果平滑的场景补丁被计算为崎岖的表面。可以区分正确的3D运动,因为它不会造成任何失真,从而产生深度不连续之间深度变化最小的背景斑块,独立运动对应的位置是崎岖不平的。提出的算法采用双目观测者,其性质被用于提取深度不连续,这一步有利于整个过程,但该技术可以通过多种方式扩展到单目观测者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Motion segmentation: a synergistic approach
Since estimation of camera motion requires knowledge of independent motion, and moving object detection and localization requires knowledge about the camera motion, the two problems of motion estimation and segmentation need to be solved together in a synergistic manner. This paper provides an approach to treating both these problems simultaneously. The technique introduced here is based on a novel concept, "scene ruggedness" which parameterizes the variation in estimated scene depth with the error in the underlying three-dimensional (3D) motion. The idea is that incorrect 3D motion estimates cause distortions in the estimated depth map, and as a result smooth scene patches are computed as rugged surfaces. The correct 3D motion can be distinguished, as it does not cause any distortion and thus gives rise to the background patches with the least depth variation between depth discontinuities, with the locations corresponding to independent motion being rugged. The algorithm presented employs a binocular observer whose nature is exploited in the extraction of depth discontinuities, a step that facilitates the overall procedure, but the technique can be extended to a monocular observer in a variety of ways.
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