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Rigid body segmentation and shape description from dense optical flow under weak perspective

机译:弱透视下从密集光流进行的刚体分割和形状描述

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摘要

We present an algorithm for identifying and tracking independently moving rigid objects from optical flow. Some previous attempts at segmentation via optical flow have focused on finding discontinuities in the flow field. While discontinuities do indicate a change in scene depth, they do not in general signal a boundary between two separate objects. The proposed method uses the fact that each independently moving object has a unique epipolar constraint associated with its motion. Thus motion discontinuities based on self-occlusion can be distinguished from those due to separate objects. The use of epipolar geometry allows for the determination of individual motion parameters for each object as well as the recovery of relative depth for each point on the object. The algorithm assumes an affine camera where perspective effects are limited to changes in overall scale. No camera calibration parameters are required. A Kalman filter based approach is used for tracking motion parameters with time.
机译:我们提出了一种从光流中识别和跟踪独立移动刚性物体的算法。先前通过光流进行分割的一些尝试集中在寻找流场中的不连续性。尽管不连续确实表明了场景深度的变化,但它们通常并不表示两个单独对象之间的边界。所提出的方法利用了以下事实:每个独立移动的对象都具有与其运动相关的唯一对极约束。因此,可以将基于自闭塞的运动不连续性与由于对象分离而引起的运动不连续性区分开。对极几何形状的使用允许确定每个对象的各个运动参数,以及恢复对象上每个点的相对深度。该算法假定仿射相机的透视效果仅限于整体比例的变化。不需要相机校准参数。基于卡尔曼滤波器的方法用于随时间跟踪运动参数。

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