A low cost foreground object detection architecture design with Multi-model Background Maintenance algorithm

Tsunq Han Tsai, De Zhang Peng, Chung Yuan Lin, Wen Tsai Sheu

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This paper proposed an architecture design for a low cost foreground object detection based on Multi-model Background Maintenance (MBM) algorithm. The MBM algorithm framework basically contains two principal features. The principal 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 table size in a low cost consideration. The total gate count of the foreground object detection architecture design is about 14.4K gate with TSMC 0.18 μm technology. The maximum operation frequency of our design is up to 100MHz.

Original languageEnglish
Pages (from-to)241-250
Number of pages10
JournalInternational Journal of Electrical Engineering
Volume16
Issue number3
StatePublished - Jun 2009

Keywords

  • Foreground object detection
  • Gaussian density function
  • MBM

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