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Depth-based detection with region comparison features

机译：具有区域比较功能的基于深度的检测

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Most object detection approaches proposed over the years rely on visual features that help to segregate objects from their backgrounds. For instance, segregation may be facilitated by depth features because they provide direct access to the third dimension. Such access enables accurate object-background segregation. Although they provide a rich source of information, depth images are sensitive to background noise. This paper addresses the issue of handling background noise for accurate foreground-background segregation. It presents and evaluates the Region Comparison (RC) features for fast and accurate body part detection. RC features are depth features inspired by the well-known Viola Jones detector. Their performances are compared to the recently proposed Pixel Comparison (PC) features, which were designed for fast and accurate object detection from Kinect-generated depth images. The results of our evaluation reveal that RC features outperform PC features in detection accuracy and computational efficiency. From these results we may conclude that RC features are to be preferred over PC features to achieve accurate and fast object detection in noisy depth images. (C) 2016 Elsevier Inc. All rights reserved.
机译：多年来提出的大多数对象检测方法都依赖于有助于将对象与其背景分离的视觉特征。例如，深度特征可促进分离，因为深度特征可直接访问三维空间。这种访问可以实现精确的对象-背景隔离。尽管深度图像提供了丰富的信息源，但它们对背景噪声很敏感。本文讨论了处理背景噪声以实现准确的前景-背景隔离的问题。它提出并评估了“区域比较”（RC）功能，可快速准确地检测身体部位。 RC特征是受著名的Viola Jones检测器启发的深度特征。将其性能与最近提出的像素比较（PC）功能进行了比较，该功能旨在根据Kinect生成的深度图像进行快速，准确的目标检测。我们的评估结果表明，在检测精度和计算效率方面，RC功能优于PC功能。从这些结果中，我们可以得出结论，在嘈杂的深度图像中实现RC的功能要比PC的功能更可取，以实现准确，快速的目标检测。 （C）2016 Elsevier Inc.保留所有权利。

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