Moving Objects Detection Based on Three Frames Differencing and GMM

Ze Xian Ke; Han Hong Jiang; Chao Liang Zhang

doi:10.4028/www.scientific.net/AMR.694-697.1974

Paper Titles

A Novel Architecture of Smart Camera
p.1953

Study for Workpiece Identity Based on the BP Neural Network and the Zernike Moment
p.1958

Number and Colors Recognition for the Beaded Pad
p.1964

Research Status on Road Information Extraction from High Resolution Imagery
p.1970

Moving Objects Detection Based on Three Frames Differencing and GMM
p.1974

Research of Glass Fiber Textile Monitor Image Recognition Based on Neural Network
p.1978

Video Noise Estimation Based on Block Neighborhood Relevance
p.1983

Multi-View Vehicle Recognition Based on WRT-SVM
p.1987

Improved Genetic Algorithm to Calibration of the Pulse Coupled Neural Network Parameter
p.1993

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 694-697Moving Objects Detection Based on Three Frames...

Moving Objects Detection Based on Three Frames Differencing and GMM

Abstract:

The paper proposes a new method for moving objects detection based on fusion of three frames differencing and Gaussian Mixture Model (GMM). In the method, two images are obtained by three frames differencing, then the adaptive background are modeled and updated by GMM for each pixel in the two differencing images. Next, two differencing images are done logic "and" operation to get the shape of the moving object. Finally adopt the mathematical morphology operation to eliminate noise and the small areas of non-objects motion parts. The simulation results show that the proposed method can detect the objects effectively and real-time. So it can be applied in visual surveillance system effectively.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 694-697)

Pages:

1974-1977

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.694-697.1974

Citation:

Cite this paper

Online since:

May 2013

Authors:

Ze Xian Ke, Han Hong Jiang, Chao Liang Zhang

Keywords:

Background Updating, Gaussian Mixture Model, Moving Objects Detection, Three Frames Differencing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Zhiqiang Wei, Xiaopeng Jiang, Peng Wang. Real-time moving object detection for video monitoring systems [J].Journal of Systems Engineering and Electronics, 2006, 17(4): 731-736

DOI: 10.1016/s1004-4132(07)60007-3

Google Scholar

[2] Zoran Z. Improved adaptive Gaussian mixture model for background subtraction [J]. Proc．of International Conference on Image Proeessing, 2003, 3: 973-976．

Google Scholar

[3] Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking[C].ICCV. 1999:246-252.

Google Scholar

[4] Zivkovie Z,Heijden F v d. Efficient adaptive density estimation per image pixel for the task of background subtraction[J]. Pattern Recognition Letters,2006,27(7):773-780.

DOI: 10.1016/j.patrec.2005.11.005

Google Scholar

[5] H. Wang, D. Suter. A reevaluation of mixture of Gaussian background modeling [video signal processing applications [J]. IEEE Proceedings of ICASSP'05．May 2005,2(2)：1017-1020

DOI: 10.1109/icassp.2005.1415580

Google Scholar

[6] Daniela Hall, J. Nascimento, P. Ribeieo, et al. Comparison of target detection algorithms using adaptive background models [J] ． International workshop on Performance evaluation of Tracking and Surveillance, Oct．2005,5 (25): 564~577

DOI: 10.1109/vspets.2005.1570905

Google Scholar

[7] Rafael C. Gonzalez. 《digital image processing》 [M], Beijing : Publishing House of Electronics Industry, 2006.4.

Google Scholar