Advertisement

The Improved Gaussian Mixture Model for Real-Time Detection System of Moving Object

  • Zhiwei Tang
  • Yunfei Cheng
  • Longhu Chen
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 422)

Abstract

Detection of moving objects is a kind of segmentation techniques based on regional characteristic such as color, gray, texture, which is the key technology in analyzing and processing of video image. A real-time motion detection method based on improved Gaussian Mixture Model is presented in this paper which is optimized and structure adjusted from Gaussian Mixture Model. Gaussian Mixture Model has been widely used in complex background scene modeling, especially in some occasions with small repetitive motion, such as shaking of the leaves, a rotating fan, bushes, the sea waves, rain, snow, etc.

Keywords

Gaussian mixture model Real-time Motion detection 

Notes

Acknowledgements

This work was financially supported by National Science and Technology Major Project (No. 2013ZX010033002-003); the Technology Research Program of Ministry of Public Security (No. 2015JSYJC21). And this work has been partially sponsored by the Technology Research Program of Ministry of Public Security of the People’s Republic of China (2014QZX005).

References

  1. 1.
    AN Bo-wen, AI Yan. Moving Object Detection And Tracking For Real-Time Video[J]. Computer Simulation. 2012; 29(2): 249–252.Google Scholar
  2. 2.
    Durte Duque, Henrique Santos,ect. Moving Object Detection Unaffected by Cast Shadows. Highlights and Ghosts. In Proc. IEEE Int. Conf. Image Processing. 2005; 413–416.Google Scholar
  3. 3.
    Cui Ying-ying. The Research on vehicle type recognition in intelligent transportation system [D]. University of Electronic Science and Technology of China. 2013 (in Chinese).Google Scholar
  4. 4.
    Zhang Yan. The Method Research and Completion of Identifying the Types of the Military Vehicle[D]. DUT.2007 (in Chinese).Google Scholar
  5. 5.
    Jolly M P D, Lakshmanan S, Jain, A K. Vehicle Segmentation and Classification Using Deformable Templates. IEEE Transaction on Pattern Analysis and Intelligence, 1996, 18(3): 293–308.Google Scholar
  6. 6.
    Huo Wei. The Research on the automatic classification of complex background models [J]. Journal of Qingdao Technological University. 2008, 01:107–110 + 115 (in Chinese).Google Scholar
  7. 7.
    Gorur. P. Speeded up Gaussian Mixture Model algorithm for background subtraction. 2011 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance(AVSS), 2011, 39(51): 386–391.Google Scholar
  8. 8.
    Yong Rui, Huang T.S, Mehrotra S. Exploring video structure beyond the shots, Multimedia Computing and Systems, 2010, pp. 237–240.Google Scholar
  9. 9.
    Albert Ahumada, Maria Chatzigiorgaki. A visual detection model for DCT coefficient quantization [J]. 9th Computing in Aerospace Conference, 2008. 22(8): 809–830.Google Scholar
  10. 10.
    X. Luo, Zheng Xu, J. Yu, and X. Chen. Building Association Link Network for Semantic Link on Web Resources. IEEE transactions on automation science and engineering, 2011, 8(3), 482–494.Google Scholar
  11. 11.
    C. Hu, Zheng Xu, et al. Semantic Link Network based Model for Organizing Multimedia Big Data. IEEE Transactions on Emerging Topics in Computing, 2014, 2(3), 376–387.Google Scholar
  12. 12.
    Zheng Xu et al. Semantic based representing and organizing surveillance big data using video structural description technology. The Journal of Systems and Software, 2015, 102, 217–225.Google Scholar
  13. 13.
    Zheng Xu et al. Knowle: a Semantic Link Network based System for Organizing Large Scale Online News Events. Future Generation Computer Systems, 2015, 43–44, 40–50.Google Scholar
  14. 14.
    Zheng Xu et al. Semantic Enhanced Cloud Environment for Surveillance Data Management using Video Structural Description. Computing, 98(1–2): 35–54, 2016.Google Scholar
  15. 15.
    C. Hu, Zheng Xu, et al. Video Structured Description Technology for the New Generation Video Surveillance System. Frontiers of Computer Science, 2015, 9(6): 980–989.Google Scholar
  16. 16.
    Zheng Xu et al. Crowd Sensing Based Semantic Annotation of Surveillance Videos, International Journal of Distributed Sensor Networks, Volume 2015 (2015), Article ID 679314, 9 pages.Google Scholar
  17. 17.
    Zheng Xu et al. Crowdsourcing based Description of Urban Emergency Events using Social Media Big Data. IEEE Transactions on Cloud Computing,  10.1109/TCC.2016.2517638.

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.The Third Research Institute of Ministry of Public SecurityShanghaiChina

Personalised recommendations