Hardware implementation of the Gaussian Mixture Model foreground object segmentation algorithm working with ultra-high resolution video stream in real-time

In this paper a hardware implementation of the Gaussian Mixture Model algorithm for background modelling and foreground object segmentation is presented. The proposed vision system is able to handle video stream with resolution up to 4K ($3840 \times 2160$ pixels) and 60 frames per second. Moreover, the constraints caused by memory bandwidth limit are also discussed and a few different solutions to tackle this issue have been considered. The designed modules have been verified on the ZCU 102 development board with Xilinx Zynq UltraScale+ MPSoC device. Additionally, the computing performance and power consumption have been estimated.