Background and foreground modeling using nonparametric kernel density estimation for visual surveillance
Background and foreground modeling using nonparametric kernel density estimation for visual surveillance
复制标题
DOI：
10.1109/jproc.2002.801448
复制
发表时间：
2002-07-01
影响因子：
20.6
通讯作者：
Davis, LS
中科院分区：
文献类型：
--
作者：
Elgammal, A;Duraiswami, R;Davis, LS
自动理解现场发生的事件是许多视觉监控系统的最终目标。对事件的更高层次的理解需要执行某些较低层次的计算机视觉任务。这些可能包括检测异常运动、跟踪目标、标记身体部位以及了解人与人之间的互动。要实现这些任务中的许多任务，有必要构建场景中对象外观的表示。本文重点讨论了与此相关的两个问题。首先，我们构建了一个统计表示的场景背景，支持灵敏的检测场景中的运动物体，但所产生的自然场景变化的杂波是强大的。其次，我们建立的前景区域（移动对象），支持他们的跟踪和支持遮挡推理的统计表示。与背景和前景相关联的概率密度函数（pdf）很可能随图像而变化，并且通常将不具有已知的参数形式。因此，我们利用一般的非参数核密度估计技术来建立这些统计表示的背景和前景。这些技术直接从数据中估计pdf，而无需对底层分布进行任何假设。给出了应用程序的示例结果。
Automatic understanding of events happening at a site is the ultimate goal for many visual surveillance systems. Higher level understanding of events requires that certain lower level computer vision tasks be performed. These may include detection of unusual motion, tracking targets, labeling body parts, and understanding the interactions between people. To achieve many of these tasks, it is necessary to build representations of the appearance of objects in the scene. This paper focuses on two issues related to this problem. First, we construct a statistical representation of the scene background that supports sensitive detection of moving objects in the scene, but is robust to clutter arising out of natural scene variations. Second, we build statistical representations of the foreground regions (moving objects) that support their tracking and support occlusion reasoning. The probability density functions (pdfs) associated with the background and foreground are likely to vary from image to image and will not in general have a known parametric form. We accordingly utilize general nonparametric kernel density estimation techniques for building these statistical representations of the background and the foreground. These techniques estimate the pdf directly from the data without any assumptions about the underlying distributions. Example results from applications are presented.