Architecture design for a low-cost and low-complexity foreground object segmentation with multi-model background maintenance algorithm

Authors:
De-Zhang Peng;Chung-Yuan Lin;Wen-Tsai Sheu;Tsung-Han Tsai
Affiliations:
Department of electrical engineering, National central university, Taiwan, R.O.C.;Department of electrical engineering, National central university, Taiwan, R.O.C.;Department of electrical engineering, National central university, Taiwan, R.O.C.;Department of electrical engineering, National central university, Taiwan, R.O.C.
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

This paper presents an architecture design for a low cost and low complexity foreground object detection based on Multi-model Background Maintenance (MBM) algorithm [1]. 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 14.4K gates with TSMC 0.18 µm technology. The operation frequency of this design is up to 100MHz.