Adaptive learning rate GMM for moving object detection in outdoor surveillance for sudden illumination changes

被引：0

作者：

Hocine L. ^{[1
]}

Cao W. ^{[1
]}

Ding Y. ^{[1
]}

Zhang J. ^{[1
]}

Luo S.-L. ^{[1
]}

机构：

[1] School of Information and Electronics, Beijing Institute of Technology, Beijing

来源：

Journal of Beijing Institute of Technology (English Edition) | 2016年 / 25卷 / 01期

关键词：

Background modeling; Frame difference; Gaussian mixture model (GMM); Learning rate; Object detection;

D O I：

10.15918/j.jbit1004-0579.201625.0121

中图分类号：

学科分类号：

摘要：

A dynamic learning rate Gaussian mixture model (GMM) algorithm is proposed to deal with the problem of slow adaption of GMM in the case of moving object detection in the outdoor surveillance, especially in the presence of sudden illumination changes. The GMM is mostly used for detecting objects in complex scenes for intelligent monitoring systems. To solve this problem, a mixture Gaussian model has been built for each pixel in the video frame, and according to the scene change from the frame difference, the learning rate of GMM can be dynamically adjusted. The experiments show that the proposed method gives good results with an adaptive GMM learning rate when we compare it with GMM method with a fixed learning rate. The method was tested on a certain dataset, and tests in the case of sudden natural light changes show that our method has a better accuracy and lower false alarm rate. © 2016 Beijing Institute of Technology.

引用

页码：145 / 151

页数：6

共 14 条

[1]

Stauffer C., Grimson W.E.L., Adaptive background mixture models for real-time tracking, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (1999)

[2]

Sheng Z., Cui X., An adaptive learning rate GMM for background extraction, IEEE International Conference on computer Science and Software Engineering, (2008)

[3]

Suo P., Wang Y., An improved adaptive backround modeling algorithm based on gaussian mixture model, International Conference on Signal Processing, (2008)

[4]

Mahfuzul H., Manzur M., Manoranjan P., Improved gaussian mixtures for robust object deection by adaptive multi-background generation, International Conference on Pattern Recognition, (2008)

[5]

Li Y., Xiong C., Yin Y., Et al., Moving object detection based on edged mixture gaussian models, International Workshop on Intelligent Systems and Applications, (2009)

[6]

Huang T., Qiu J., Sakayori T., Et al., Motion detection based on background modeling and performance analysis for outdoor surveillance, International Conference on computer Modeling and Simulation, (2009)

[7]

Mayssa A., Soumil G., Magdy B., A hybrid adaptive scheme based on selective gaussian modeling for real-time object detection, International Symposium on Circuits and Systems, (2009)

[8]

Huang M., Chen G., Yang G.T., Et al., An algorithm of the target detection and tracking of the video, International Workshop on Information and Electronics Engineering, (2012)

[9]

Jiao B., Yan L., Li W., Fast convergent gaussian mixture model in moving objects detection, International Conference on Computer Science and Automation Engineering, (2011)

[10]

Mukherjee S., Das K., An adaptive GMM approach to background subtraction for application in real time surveillance, International Journal of Research in Engineering and Technology, 2, 1, pp. 25-29, (2013)

← 1 2 →