  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.
