Background modeling and subtraction of dynamic scenes
Background modeling and subtraction of dynamic scenes
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DOI：
10.1109/iccv.2003.1238641
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发表时间：
2003-10
期刊：
影响因子：
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通讯作者：
Antoine Monnet;Anurag Mittal;N. Paragios;Visvanathan Ramesh
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文献类型：
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作者：
Antoine Monnet;Anurag Mittal;N. Paragios;Visvanathan Ramesh
背景建模和减法是运动分析中的核心组成部分。这种模块背后的核心思想是创建静态场景的概率表示，并将其与当前输入进行比较以执行减法。当要建模的场景是指有限扰动的静态结构时，这种方法是有效的。在本文中，我们解决了建模静态背景假设无效的动态场景的问题。挥舞着树木，海滩，自动扶梯，自然风光或降雪的自然场景就是例子。受Doretto等人提出的工作的启发。 （2003年），我们提出了一个在线自动回归模型，以捕获和预测此类场景的行为。为了检测事件，我们介绍了一个基于国家驱动的预测与实际框架之间的比较的新指标。有希望的结果证明了所提出的框架的潜力。
Background modeling and subtraction is a core component in motion analysis. The central idea behind such module is to create a probabilistic representation of the static scene that is compared with the current input to perform subtraction. Such approach is efficient when the scene to be modeled refers to a static structure with limited perturbation. In this paper, we address the problem of modeling dynamic scenes where the assumption of a static background is not valid. Waving trees, beaches, escalators, natural scenes with rain or snow are examples. Inspired by the work proposed by Doretto et al. (2003), we propose an on-line auto-regressive model to capture and predict the behavior of such scenes. Towards detection of events we introduce a new metric that is based on a state-driven comparison between the prediction and the actual frame. Promising results demonstrate the potentials of the proposed framework.