论文标题
在GPU上实现的RGB-D数据中的前景对象分割
Foreground object segmentation in RGB-D data implemented on GPU
论文作者
论文摘要
本文介绍了两个前景对象分割算法的GPU实现：高斯混合模型（GMM）和基于像素的自适应分段（PBA）（PBAS）修改了用于RGB-D数据支持。同时使用颜色（RGB）和深度（D）数据可以提高分割精度，尤其是在颜色伪装，照明变化和阴影的发生时。三个GPU用于加速计算：嵌入式NVIDIA JETSON TX2（MAXWELL体系结构），移动NVIDIA GEFORCE GTX 1050M（PASCAL架构）和有效的NVIDIA RTX 2070（Turing Architture）。分割精度与先前发表的作品相当。此外，允许使用GPU平台进行实时图像处理。此外，该系统已经适应了两个RGB-D传感器：来自Intel的Realsense D415和D435。
This paper presents a GPU implementation of two foreground object segmentation algorithms: Gaussian Mixture Model (GMM) and Pixel Based Adaptive Segmenter (PBAS) modified for RGB-D data support. The simultaneous use of colour (RGB) and depth (D) data allows to improve segmentation accuracy, especially in case of colour camouflage, illumination changes and occurrence of shadows. Three GPUs were used to accelerate calculations: embedded NVIDIA Jetson TX2 (Maxwell architecture), mobile NVIDIA GeForce GTX 1050m (Pascal architecture) and efficient NVIDIA RTX 2070 (Turing architecture). Segmentation accuracy comparable to previously published works was obtained. Moreover, the use of a GPU platform allowed to get real-time image processing. In addition, the system has been adapted to work with two RGB-D sensors: RealSense D415 and D435 from Intel.
