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首页> 外文会议>Conference on Real-Time Image Processing; 20080128-29; San Jose,CA(US) >Motion estimation through efficient matching of a reduced number of reliable singular points
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Motion estimation through efficient matching of a reduced number of reliable singular points

机译：通过有效匹配数量减少的可靠奇异点进行运动估计

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摘要

Motion estimation in video sequences is a classical intensive computational task that is required for a wide range of applications. Many different methods have been proposed to reduce the computational complexity, but the achieved reduction is not enough to allow real time operation in a non-specialized hardware. In this paper an efficient selection of singular points for fast matching between consecutive images is presented, which allows to achieve real time operation. The selection of singular points lies in finding the image points that are robust to the noise and the aperture problem. This is accomplished by imposing restrictions related to the gradient magnitude and the cornerness. The neighborhood of each singular point is characterized by a complex descriptor vector, which presents a high robustness to illumination changes and small variations in the 3D camera viewpoint. The matching between singular points of consecutive images is performed by maximizing a similarity measure based on the previous descriptor vector. The set of correspondences yields a sparse motion vector field that accurately outlines the image motion. In order to demonstrate the efficiency of this approach, a video stabilization application has been developed, which uses the sparse motion vector field as input. Excellent results have been obtained in synthetic and real sequences, demonstrating the efficiency of the proposed motion estimation technique.
机译：视频序列中的运动估计是一项经典的密集计算任务，需要广泛的应用。已经提出了许多不同的方法来降低计算复杂性，但是所实现的降低还不足以允许在非专用硬件中进行实时操作。在本文中，提出了一种有效的奇异点选择，用于连续图像之间的快速匹配，这可以实现实时操作。奇异点的选择在于找到对噪声和孔径问题具有鲁棒性的像点。这是通过施加与梯度大小和转角有关的限制来实现的。每个奇异点的邻域都由一个复杂的描述符向量来表征，该向量对照明变化和3D摄像机视点中的小变化表现出很高的鲁棒性。连续图像的奇异点之间的匹配是通过基于先前的描述符向量最大化相似性度量来执行的。这组对应关系产生一个稀疏的运动矢量场，该场精确地勾勒出图像运动的轮廓。为了证明这种方法的效率，开发了一种视频稳定应用程序，该应用程序使用稀疏运动矢量场作为输入。在合成和真实序列中均获得了出色的结果，证明了所提出的运动估计技术的效率。

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