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.

翻译：本文件介绍了一种与高山混合模型(GMM)一起对 RGBD 数据进行背景/前景分割的新办法。 我们首先从颜色和深度图像分别的背景减色开始, 然后将两个流产生的前景结合, 以便进行更准确的探测。 我们的分割解决方案在 GPU 上实施。 因此, 它以传感器( 30fps) 的完整框架速度工作 。 测试结果显示它能抵御光线变化、 阴影和反射 。