Architecture design for a low-cost and low-complexity foreground object segmentation with Multi-model Background Maintenance algorithm

This paper presents an architecture design for a low cost and low complexity foreground object detection based on Multi-model Background Maintenance (MBM) algorithm . The MBM framework basically contains two principal features. These features consist of static and dynamic pixels to represent the characteristic of background. Under this framework, a pure time-varying background image is maintained and learned using the statistical information of the multiple Gaussian distribution with principal features. In the MBM architecture, look-up table based Gaussian density function architecture is proposed. Three look-up tables are used for exponential and division of the Gaussian density function. The characteristic of Gaussian density function is also used to enormously reduce the table size in a low cost and low complexity consideration. The total gate count of the foreground object detection architecture is about 14.4 K gates with TSMC 0.18 um technology. The operation frequency of this design is up to 100 MHz.