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.