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首页> 外文会议>2010 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB 2010) >Real-time foreground detection based on tempo-spatial consistency validation and Gaussian Mixture Model
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Real-time foreground detection based on tempo-spatial consistency validation and Gaussian Mixture Model

机译：基于时空一致性验证和高斯混合模型的实时前景检测

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摘要

Robust foreground detection is a fundamental precursor of many video processing applications. Although various approaches were advanced, there still exist many factors making detection very challenging: 1) Dynamic background with gradual brightness changes, camera movement and large amount of noises. 2) Sharp illumination changes caused by shadows, light on-off, and so on. 3) Real-time requirement for practical systems. To overcome these problems, a new approach is proposed in this paper. It is based on the background of conventional Gaussian Mixed Model, incorporating tempo-spatial consistency validation to search genuine foreground seeds, so that foreground segments can be reliably acquired using region growth method. Experiments demonstrate that our approach achieves better performance than conventional GMM approach in detection accuracy, adaptability to sudden illumination changes and computation time.
机译：强大的前景检测是许多视频处理应用程序的基本先驱。尽管已经提出了各种方法，但是仍然存在许多因素使检测变得非常具有挑战性：1）动态背景，亮度逐渐变化，摄像机移动且噪声很大。 2）由阴影，光线开-关等引起的急剧的照明变化。 3）实际系统的实时需求。为了克服这些问题，本文提出了一种新的方法。它基于常规高斯混合模型的背景，结合时空一致性验证来搜索真正的前景种子，从而可以使用区域增长方法可靠地获取前景片段。实验表明，与传统的GMM方法相比，我们的方法在检测准确度，对突然照度变化和计算时间的适应性方面均达到了更好的性能。

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