A real time 1080P 30FPS Gaussian Mixture Modeling design for background subtraction and object extraction

Author

Shuo-Wen Hsu ; Tian Sheuan Chang

Author_Institution

Dept. of Electron. Eng. & Inst. of Electron., Nat. Chiao-Tung Univ., Hsinchu, Taiwan

fYear

2014

fDate

26-28 May 2014

Firstpage

185

Lastpage

186

Abstract

The Gaussian Mixture Modeling (GMM) algorithm with connected component labeling as object extraction provide robust background subtraction but suffer from complexity, and large buffer or high bandwidth due to the frame level operations. For real time application needs, this paper proposed a block based GMM design for background subtraction with message passing between blocks to avoid performance drop. The corresponding parallel hardware design can reach real time 1080P@30fps and cost 164.82K gate-counts at 125MHz with 90nm process.

Keywords

Gaussian processes; feature extraction; message passing; mixture models; object detection; GMM algorithm; Gaussian mixture modeling algorithm; background subtraction; block based GMM design; connected component labeling; frame level operations; frequency 125 MHz; message passing; object extraction; parallel hardware design; size 90 nm; Algorithm design and analysis; Bandwidth; Classification algorithms; Computer architecture; Hardware; Real-time systems; Robustness; Architecture; Background Subtraction; Gaussian Mixture Modeling;

fLanguage

English

Publisher

ieee

Conference_Titel

Consumer Electronics - Taiwan (ICCE-TW), 2014 IEEE International Conference on

Conference_Location

Taipei

Type

conf

DOI

10.1109/ICCE-TW.2014.6904050

Filename

6904050