Moving Object Detection for Visual Surveillance Using Quasi-euclidian Distance

  • Dileep Kumar Yadav
  • Karan Singh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 381)


Moving object detection is a fundamental step for visual surveillance system, other image processing, and computer vision applications. The most popular and common technique for moving foreground detection is background subtraction. In dynamic background, Gaussian Mixture Model performs better for object detection. In this work, a GMM-based background model is developed. This work proposes a quasi-euclidian distance measure in order to measure the variation in terms of distance, between modeled frame and test frame. To classify the pixel, this distance is compared with a suitable threshold. The connected component and blob labeling has been used to improve the model with a threshold. Morphological filter is used to improve the foreground information. The experimental study shows that the proposed work performs better in comparison to considered state-of-the-art methods in term precision, recall, and f-measure.


Gaussian mixture model Quasi-euclidian distance Object detection Visual surveillance Morphological filter 


  1. 1.
    Bouwmans, T., Zahzah, E.: Robust PCA via principal component pursuit: a review for a comparative evaluation in video surveillance. Comput. Vis. Image Underst. Elsevier 122, 22–34 (2014)CrossRefGoogle Scholar
  2. 2.
    Haines, T.S.F., Xiang, T.: Background subtraction with Dirichlet process mixture model. IEEE Trans. PAMI 36(4), 670–683 (2014)Google Scholar
  3. 3.
    Lee, S., Lee, C.: Low-complexity background subtraction based on spatial similarity. Eurasip J. Image Video Process. Springer 2014(1), 30 (2014)Google Scholar
  4. 4.
    Jung, C.R.: Efficient background subtraction and shadow removal for monochrome video sequences. IEEE Trans. Multimedia 11(3), 571–577 (2009)CrossRefGoogle Scholar
  5. 5.
    Stauffer, C., Grimson, W.E.L.: Adaptive background mixture models for real-time tracking. In: Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 246–252. IEEE Computer Society, Fort Collins (1999)Google Scholar
  6. 6.
    Haque, M., Murshed, M., Paul, M.: On stable dynamic background generation technique using Gaussian Mixture Models for robust object detection. In: 5th International Conference on Advanced Video and Signal Based Surveillance, pp. 41–48. IEEE, New Mexico (2008)Google Scholar
  7. 7.
    Zhou, X., Yang, C., Yu, W.: Moving object detection by detecting contiguous outliers in the low-rank representation. IEEE Trans. PAMI 35(3), 597–610 (2013)Google Scholar
  8. 8.
    Lee, D.S.: Effective Gaussian mixture learning for video background subtraction. IEEE Trans. Pattern Anal. Mach. Intell. 27, 827–832 (2005)CrossRefGoogle Scholar
  9. 9.
    Yadav, D.K.: Efficient method for moving object detection in cluttered background using Gaussian Mixture Model. In: 3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI 2014), pp. 943–948. IEEE, India (2014)Google Scholar
  10. 10.
    Charles, P.L., Bilodeau, G.A., Bergevin, R.: Subsense: a universal change detection method with local adaptive sensitivity. IEEE Trans. Image Process. 24(1), 359–373 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Khanna, A.N., Ghosh, R., Kumar, S., Das, A., Sardana, H.K.: Adaptive contour-based statistical background subtraction method for moving target detection in infrared video sequences. Infrared Phys. Technol. Elsevier 63, 103–109 (2014)CrossRefGoogle Scholar
  12. 12.
    Goyette, N., Jodoin, P.M., Porikli, F., Ishwar, P.: Change detection—a new change detection benchmark database. In: Proceedings of IEEE Workshop on Change Detection at CVPR, pp. 1–8, USA (2012)Google Scholar
  13. 13.
    Toyama, K., Krumm, J., Brumitt, B., Meyers, B.: Wallflower: principles and practice of background maintenance. In 7th International Conference on Computer Vision, pp. 255–261. IEEE Computer Society Press, Greece (1999)Google Scholar
  14. 14.
    Yadav, D.K., Sharma, L.S., Bharti, S.K.: Moving object detection in real-time visual surveillance using background subtraction technique. In: 14th International Conference on Hybrid Intelligent Systems, pp. 79–84. IEEE, Kuwait (2014) (IEEE Catalog Number: CFP14360-ART; ISBN: 978-1-4799-7633-1)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.School of Computer and Systems Sciences, JNUNew DelhiIndia

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