Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction
Adaptively Splitted GMM With Feedback Improvement for the Task of Background Subtraction
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DOI：
10.1109/tifs.2014.2313919
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发表时间：
2014-05
影响因子：
6.8
通讯作者：
Rubén Heras Evangelio;Michael Pätzold;I. Keller;T. Sikora
中科院分区：
文献类型：
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作者：
Rubén Heras Evangelio;Michael Pätzold;I. Keller;T. Sikora
每像素自适应高斯混合模型（GARCH）已成为一个流行的选择，在视频监控领域的变化检测，因为他们的能力，以科普许多挑战的特点，在真实的时间与低内存要求的监控系统。自GMM首次引入监控领域以来，GMM在许多方面都得到了增强。在本文中，我们提出了一些相关的GMM方法的研究，并分析其基本假设和设计决策。在本文的基础上，我们展示了如何这些系统可以进一步改善通过方差控制方案和纳入区域分析为基础的反馈。建议的系统已被彻底评估，使用广泛的数据集的IEEE研讨会上的变化检测，表现出超越性能相比，国家的最先进的方法。
Per pixel adaptive Gaussian mixture models (GMMs) have become a popular choice for the detection of change in the video surveillance domain because of their ability to cope with many challenges characteristic for surveillance systems in real time with low memory requirements. Since their first introduction in the surveillance domain, GMM has been enhanced in many directions. In this paper, we present a study of some relevant GMM approaches and analyze their underlying assumptions and design decisions. Based on this paper, we show how these systems can be further improved by means of a variance controlling scheme and the incorporation of region analysis-based feedback. The proposed system has been thoroughly evaluated using the extensive data set of the IEEE Workshop on Change Detection, showing an outranking performance in comparison with state-of-the-art methods.