基于范围和颜色的背景估计和去除

G. Gordon, Trevor Darrell, M. Harville, J. Woodfill
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引用次数: 193

摘要

基于距离和颜色数据联合使用的背景估计和去除比单独使用任何一种数据源都能产生更好的结果。随着新型硬件和CPU处理速度的提高,廉价、实时、无源测距系统变得越来越容易使用,这一点越来越重要。距离是一个强大的分割信号,它在很大程度上独立于颜色,因此不受传统的阴影和与背景颜色相似的物体的颜色分割问题的影响。然而,距离本身也不足以实现良好的分割:在场景中几乎无法对所有像素进行深度测量,当前景物体靠近背景时,它们可能在深度上无法区分。在这些情况下,颜色分割是互补的。令人惊讶的是,迄今为止在联合范围和颜色分割方面做的工作很少。我们描述并演示了一种基于每个图像像素的多维(范围和颜色)聚类的背景估计方法。在给定帧中,前景的分割是通过与背景统计的范围和归一化颜色的比较来完成的。详细讨论了诸如阴影处理和低置信度测量等重要的实现问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Background estimation and removal based on range and color
Background estimation and removal based on the joint use of range and color data produces superior results than can be achieved with either data source alone. This is increasingly relevant as inexpensive, real-time, passive range systems become more accessible through novel hardware and increased CPU processing speeds. Range is a powerful signal for segmentation which is largely independent of color and hence not effected by the classic color segmentation problems of shadows and objects with color similar to the background. However range alone is also not sufficient for the good segmentation: depth measurements are rarely available at all pixels in the scene, and foreground objects may be indistinguishable in depth when they are close to the background. Color segmentation is complementary in these cases. Surprisingly, little work has been done to date on joint range and color segmentation. We describe and demonstrate a background estimation method based on a multidimensional (range and color) clustering at each image pixel. Segmentation of the foreground in a given frame is performed via comparison with background statistics in range and normalized color. Important implementation issues such as treatment of shadows and low confidence measurements are discussed in detail.
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