Hardware Performance Versus Video Quality Trade-Off for Gaussian Mixture Model Based Background Identification Systems

Background identification is a fundamental task in many video processing systems. The Gaussian Mixture Model is a background identification algorithm that models the pixel luminance with a mixture of K Gaussian distributions. The number of Gaussian distributions determines the accuracy of the background model and the computational complexity of the algorithm. This paper compares two hardware implementations of the Gaussian Mixture Model that use three and five Gaussians per pixel. A trade off analysis is carried out by evaluating the quality of the processed video sequences and the hardware performances. The circuits are implemented on FPGA by exploiting state of the art, hardware oriented, formulation of the Gaussian Mixture Model equations and by using truncated binary multipliers. The results suggest that the circuit that uses three Gaussian distributions provides video with good accuracy while requiring significant less resources than the option that uses five Gaussian distributions per pixel.