Mixed-motion Segmentation Using Helmholtz Decomposition
Mixed-motion Segmentation Using Helmholtz Decomposition
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DOI：
10.2197/ipsjtcva.5.55
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
Cuicui Zhang;Xuefeng Liang;T. Matsuyama
中科院分区：
文献类型：
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作者：
Cuicui Zhang;Xuefeng Liang;T. Matsuyama
运动分割在视频监控、人机交互、动作识别等视频分析中起着核心作用。对固定摄像机场景进行了广泛的研究。近年来，动态背景的研究越来越受到人们的关注。在许多应用中，背景运动不太重要，只期望局部物体运动。对于全局运动估计和运动分割，已经提出了几种方法（全局运动和目标运动也分别称为内线和离群点）。[6]中的工作引入了一种参数形式，它假设了从简单的平移到使用不同参数的一般视角变换的全局运动模型。在[2]中提出了一种联合全局运动估计和分割方法。它通过分割出离群值来迭代更新初始模型。采用梯度下降（GD）[6]或最小二乘（LS）[5]的回归方案来改进早期模型。[1]中的异常值抑制滤波器通过在预定义窗口中检查运动向量的相似性来显式过滤运动向量。[3]中的RANSAC是一种通过迭代更新初始化概率来估计初始化模型的统计方法。以上方法都是基于二维的方法。它们要求多个运动是独立的。但对于相互依存的运动，它们可能无法处理。考虑到这一点，最好将不同的运动放置在高维空间的不同层上。为此，我们的方法将二维运动场转换为三维曲面。表面上的局部极值，如峰、脊和谷，描绘了局部运动，而平滑的地方代表了全局运动。三维曲面的计算采用亥莫霍兹分解。在[4]中，提出了一种基于亥姆霍兹分解的粒子滤波器用于流量估计。以下是其基本理论。对于任意流场ξ，将其分解为两个分量：无旋度（散度）分量∇E和无散度（旋度）分量∇x W，其中E和W是我们想要得到的三维势面。的
Motion segmentation plays a central role in video analysis, such as the surveillance, human-computer interaction, action recognition, etc. Extensive studies have been done on the stationary camera scenarios. Recently, more attentions are focusing on dynamic backgrounds with several moving objects in the scene. In many applications, the background motion is of much less interest, and solely the local object motion is expected. Several approaches have been proposed for global motion estimation and motion segmentation (the global motion and the object motion are also named as inlier and outlier, respectively). The work in [6] introduced a parametric form which assumed the global motion model from simple translation to general perspective transformation using different parameters. A joint global motion estimation and segmentation method was proposed in [2]. It iteratively updates the inlier model by segmenting the outlier out. A regression scheme, using gradient descent (GD) [6] or least squares (LS) [5], is also applied to refine the inlier model. An outlier rejection filter in [1] explicit filters motion vectors by checking their similarity in a pre-defined window. RANSAC in [3] is a statistical method which estimates the inlier model by iteratively updating the probability of inlier. All above methods are 2D based methods. They require the multiple motions to be independent. But for interdependent motions, they may fail to deal with. Considering this, it is better to place different motions on different layers in higher dimensional space. To this end, our method transforms 2D motion field into 3D surfaces. Local extremes on the surface such as peaks, ridges and valleys depict local motions while smoothing places represent the global motion. The 3D surface is calculated using Helmoholtz decomposition. In [4], a particle filter based on Helmholtz decomposition was proposed for flow estimation. Following is its fundamental theory. For an arbitrary flow field ξ, it is decomposed into two components: curl-free (divergence) component ∇E and divergence-free (curl) component ∇ × W , where E and W are what we want to obtain the 3D potential surfaces. The