Object detection based on a robust and accurate statistical multi-point-pair model
Object detection based on a robust and accurate statistical multi-point-pair model
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DOI：
10.1016/j.patcog.2010.11.022
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Xinyue Zhao;Y. Satoh;H. Takauji;S. Kaneko;K. Iwata;R. Ozaki
中科院分区：
文献类型：
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作者：
Xinyue Zhao;Y. Satoh;H. Takauji;S. Kaneko;K. Iwata;R. Ozaki
在本文中，我们提出了一种稳健且准确的背景模型，称为灰度排列对（GAP）。该模型基于统计范围特征（SRF），它被定义为一组统计成对特征。使用GAP模型，可以在各种复杂的环境条件下成功检测移动物体。该方法的主要概念是使用表现出稳定统计强度关系的多个点对作为背景模型。成对像素之间的强度差比单个像素的强度差稳定得多，尤其是在不同的环境中。我们提出的方法更关注像素之间全局空间相关性的历史，而不是任何给定像素或局部空间相关性的历史。此外，我们阐明了如何减少 GAP 建模时间，并给出了将 GAP 与现有目标检测方法进行比较的实验结果，证明 GAP 实现了具有更高精确度和召回率的卓越目标检测。
In this paper, we propose a robust and accurate background model, called grayscale arranging pairs (GAP). The model is based on the statistical reach feature (SRF), which is defined as a set of statistical pair-wise features. Using the GAP model, moving objects are successfully detected under a variety of complex environmental conditions. The main concept of the proposed method is the use of multiple point pairs that exhibit a stable statistical intensity relationship as a background model. The intensity difference between pixels of the pair is much more stable than the intensity of a single pixel, especially in varying environments. Our proposed method focuses more on the history of global spatial correlations between pixels than on the history of any given pixel or local spatial correlations. Furthermore, we clarify how to reduce the GAP modeling time and present experimental results comparing GAP with existing object detection methods, demonstrating that superior object detection with higher precision and recall rates is achieved by GAP.