@inproceedings{5706bfd074634144839863937397afab,
title = "Architecture design for a low-cost and low-complexity foreground object segmentation with multi-model background maintenance algorithm",
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 um technology. The operation frequency of this design is up to 100MHz.",
keywords = "Foreground object segmentation, Gaussian density function, Multi-model background maintenance",
author = "Peng, {De Zhang} and Lin, {Chung Yuan} and Sheu, {Wen Tsai} and Tsai, {Tsung Han}",
year = "2009",
doi = "10.1109/ICIP.2009.5414350",
language = "???core.languages.en_GB???",
isbn = "9781424456543",
series = "Proceedings - International Conference on Image Processing, ICIP",
publisher = "IEEE Computer Society",
pages = "3241--3244",
booktitle = "2009 IEEE International Conference on Image Processing, ICIP 2009 - Proceedings",
note = "2009 IEEE International Conference on Image Processing, ICIP 2009 ; Conference date: 07-11-2009 Through 10-11-2009",
}