Research Article | OPEN ACCESS
Video Background Extraction Using Improved Mean Algorithm and Frame Difference Method
1, 2Ahmed Mahgoub Ahmed Talab, 1Zhangcan Huang and 1Xiong Jiang
1School of Sciences, Wuhan University of Technology, Wuhan, China
2College of Engineering, Elimam ELMahdi University, Sudan
Research Journal of Applied Sciences, Engineering and Technology 2014 22:4795-4800
Received: January 24, 2014 | Accepted: February 10, 2014 | Published: June 10, 2014
Abstract
Background extraction is a crucial step in many automatic video content analysis applications. In this study, we propose new tracking approach by usage of two sequential images in limited period and by giving the specific threshold; then if the difference is greater than the threshold, the extracted image is classified as foreground image while if it is less than the threshold it classified as background. In addition, we propose extraction of background image from video using the improved mean algorithm. The Experimental results proof that the proposed method is fast and no shadow was recorded.
Keywords:
Background extraction, frame difference method, mean algorithm,
References
-
Bazzani, L., D. Bloisi and V. Murino, 2009. A comparison of multi hypothesis kalman filter and particle filter for multi-target tracking. Proceeding of the Performance Evaluation of Tracking and Surveillance Workshop at CVPR, pp: 47-54.
-
Bondzulic, B. and V. Petrovic, 2008. Multisensor background extraction and updating for moving target detection. Proceeding of the 11th International Conference on Information Fusion, pp: 1-8.
-
Fan, Y., 2005. A real-time algorithm of dynamic background extraction in image sequence. Proceedings of the International Conference on Machine Learning and Cybernetics. Guangzhou, China, 8: 4997-5000.
-
Kravchonok, A., 2012. Region-growing detection of moving objects in video sequences based on optical flow. S. Mach. Perc., 22: 244-255.
CrossRef
-
Meijin, L., Z. Ying and H. Jiandeng, 2009. Video background extraction based on improved mode algorithm. Proceedings of the 3rd International Conference on Genetic and Evolutionary Computing (WGEC'09), pp: 331-334.
-
Wang, L. and N.H. Yung, 2010. Extraction of moving objects from their background based on multiple adaptive thresholds and boundary evaluation. IEEE T. Intell. Transp., 11: 40-51.
CrossRef
-
Wu, J., Z.M., Cui, H.J. Yue and G.M. Zhang, 2012. Semantic analysis of traffic video using image understanding. J. Multimedia, 7: 41-48.
CrossRef
-
Yang, Y.Z., X.F. He, Y.Z. Chen and Z.K. Bao, 2008. Traffic parameter detection based on video images [J]. Control Eng. China, 3: 36.
-
Yang, J., R. Stiefelhagen, U. Meier and A. Waibel, 1998. Visual tracking for multimodal human computer interaction. Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM Press/Addison-Wesley Publishing Co., New York, pp: 140-147.
CrossRef
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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