Improved Video Registration using Non-Distinctive Local Image Features

Robin Hess, Alan Fern
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引用次数: 81

Abstract

The task of registering video frames with a static model is a common problem in many computer vision domains. The standard approach to registration involves finding point correspondences between the video and the model and using those correspondences to numerically determine registration transforms. Current methods locate video-to-model point correspondences by assembling a set of reference images to represent the model and then detecting and matching invariant local image features between the video frames and the set of reference images. These methods work well when all video frames can be guaranteed to contain a sufficient number of distinctive visual features. However, as we demonstrate, these methods are prone to severe misregistration errors in domains where many video frames lack distinctive image features. To overcome these errors, we introduce a concept of local distinctiveness which allows us to find model matches for nearly all video features, regardless of their distinctiveness on a global scale. We present results from the American football domain-where many video frames lack distinctive image features-which show a drastic improvement in registration accuracy over current methods. In addition, we introduce a simple, empirical stability test that allows our method to be fully automated. Finally, we present a registration dataset from the American football domain we hope can be used as a benchmarking tool for registration methods.
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改进视频配准使用非显著的局部图像特征
在许多计算机视觉领域中,用静态模型对视频帧进行配准是一个常见问题。配准的标准方法包括找到视频和模型之间的点对应关系,并使用这些对应关系在数值上确定配准变换。目前的方法是通过组装一组代表模型的参考图像,然后在视频帧和参考图像集之间检测和匹配不变的局部图像特征来定位视频与模型点的对应关系。当所有视频帧都能保证包含足够数量的不同的视觉特征时,这些方法就能很好地工作。然而,正如我们所证明的,这些方法在许多视频帧缺乏显著图像特征的域中容易出现严重的配错错误。为了克服这些错误,我们引入了一个局部显著性的概念,使我们能够找到几乎所有视频特征的模型匹配,而不管它们在全球范围内的显著性如何。我们展示了来自美式橄榄球领域的结果,其中许多视频帧缺乏鲜明的图像特征,这表明与当前方法相比,配准精度有了极大的提高。此外,我们引入了一个简单的,经验性的稳定性测试,使我们的方法完全自动化。最后,我们提供了一个来自美式橄榄球领域的注册数据集,我们希望可以用作注册方法的基准测试工具。
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