Moving Object Detection Based on Improved Background Updating Method for Gaussian Mixture Model

Abstract:

An effective improvement method was put forward caused by the traditional Gaussian mixture model has poor adaptability to illumination mutation. When illumination mutation is detected, improved Frame difference could detect the foreground region and background region, and then adopts a new replacing update methods to the Gaussian distribution with the least weights of Gaussian mixture background models in different regions. The experimental results show that improved method makes Gaussian mixture model can quickly adaptive to the light mutation, and exactly detect the moving object.

• Periodical: Advanced Materials Research (Volumes 1049-1050)
• Pages: 1561-1565
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.1049-1050.1561
• Online since: October 2014
• Authors: Wu Wen*, Tao Jiang, Yu Fang Gou
• Keywords: Frame Difference, Gaussian Mixture Model, Illumination Mutation, Short-Stayed Object

References

[1] P. Lenz, J. Ziegler, A. Geiger, et al. Sparse scene flow segmentation for moving object detection in urban environments[C]/Intelligent Vehicles Symposium (IV), 2011 IEEE. IEEE, 2011: 926-932.

DOI: 10.1109/ivs.2011.5940558

[2] H. Guo, Y. Liang, Z. Yu, et al. Implementation and analysis of moving objects detection in Video Surveillance[C]/Information and Automation (ICIA), 2010 IEEE International Conference on. IEEE, 2010: 154-158.

DOI: 10.1109/icinfa.2010.5512354

[3] R. Yan, X. Song, S. Yan. Moving object detection based on an improved Gaussian mixture background model[C]/Computing, Communication, Control, and Management, 2009. CCCM 2009. ISECS International Colloquium on. IEEE, 2009, 1: 12-15.

DOI: 10.1109/cccm.2009.5268164

[4] G. Ma, S.B. Park, A. Ioffe, et al. A real time object detection approach applied to reliable pedestrian detection[C]/Intelligent Vehicles Symposium, 2007 IEEE. IEEE, 2007: 755-760.

DOI: 10.1109/ivs.2007.4290207

[5] C. Stauffer, W.E.L. Grimson. Adaptive background mixture models for real-time tracking[C]/Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on. IEEE, 1999, 2.

DOI: 10.1109/cvpr.1999.784637

[6] M.Z. Li, Z.Q. Ma, Y. Shan, X.Y. Zhang. Adaptive background update based on Gaussian mixture model under complex condition [J]. Journal of Computer Application, 2011, 31(7): 1831-1834.

[7] H. WANG, D. SUTER. A Re-evaluation of Mixture of Gaussian Background Modeling[C]/ Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, Pennsylvania, USA, 2005: 1017-1020.

DOI: 10.1109/icassp.2005.1415580

[8] B. Jiao, X.F. Lv, Y. Chen, Y. Li. Improved algorithm of adaptive Gaussian mixture model for real-time moving object detection [J]. Application Research of Computers, 2013, 30(11): 3518-3520.

[9] H. Zhang, H. Fang, C.G. Li. Adaptive moving vehicle detection algorithm based on improved Gaussian mixture model [J]. Computer Applications and Software, 2014, 31(1): 286-289.