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首页> 外文会议>IEEE International Symposium on Broadband Multimedia Systems and Broadcasting >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：基于天性空间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：鲁棒前景检测是许多视频处理应用的基本前兆。虽然各种方法都是先进的，但仍然存在许多因素，使得检测非常具有挑战性：1）动态背景具有逐渐亮度变化，相机运动和大量的噪音。 2）阴影，浅开关造成的尖锐照明变化。 3）实际系统的实时要求。为了克服这些问题，本文提出了一种新方法。它基于传统的高斯混合模型的背景，包括节奏空间一致性验证来搜索真正的前景种子，从而可以使用区域生长方法可靠地获取前景区段。实验表明，我们的方法在检测准确性中实现了比传统的GMM方法更好的性能，适应突然的照明变化和计算时间。

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