GPU based GMM segmentation of kinect data

Abdenour Amamra, Tarek Mouats, N. Aouf
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引用次数: 9

Abstract

This paper presents a novel approach for background/foreground segmentation of RGBD data with the Gaussian Mixture Models (GMM). We first start by the background subtraction from the colour and depth images separately. The foregrounds resulting from both streams are then fused for a more accurate detection. Our segmentation solution is implemented on the GPU. Thus, it works at the full frame rate of the sensor (30fps). Test results show its robustness against illumination change, shadows and reflections.
基于GPU的kinect数据GMM分割
提出了一种基于高斯混合模型(GMM)的RGBD数据背景/前景分割新方法。我们首先分别从颜色和深度图像中进行背景减法。然后将两种流产生的前景融合以进行更准确的检测。我们的分割解决方案是在GPU上实现的。因此,它可以在传感器的全帧速率(30fps)下工作。测试结果表明,该算法对光照变化、阴影和反射具有较强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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