ASIC and FPGA Implementation of the Gaussian Mixture Model Algorithm for Real-Time Segmentation of High Definition Video

摘要

Background identification is a common feature in many video processing systems. This paper proposes two hardware implementations of the OpenCV version of the Gaussian mixture model (GMM), a background identification algorithm. The implemented version of the algorithm allows a fast initialization of the background model while an innovative, hardware-oriented, formulation of the GMM equations makes the proposed circuits able to perform real-time background identification on high definition (HD) video sequences with frame size 1920$,times,$1080. The first of the two circuits is designed with commercial field-programmable gate-array (FPGA) devices as target. When implemented on Virtex6 vlx75t, the proposed circuit process 91 HD fps (frames per second) and uses 3% of FPGA logic resources. The second circuit is oriented to the implementation in UMC-90 nm CMOS standard cell technology, and is proposed in two versions. Both versions can process at a frame rate higher than 60 HD fps. The first version uses the constant voltage scaling technique to provide a low power implementation. It provides silicon area occupation of 28847 $mu{rm m}^{2}$ and energy dissipation per pixel of 15.3 pJ/pixel. The second version is designed to reduce silicon area utilization and occupies 21847 $mu{rm m}^{2}$ with an energy dissipation of 49.4 pJ/pixel.