Foreground Detection Using Region of Interest Analysis Based on Feature Points Processing

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10531)

Abstract

Analysis of regions of interest is a promising approach for performance improvement of many applications related to video signal processing and transmission. Many state-of-the-art methods face challenges like global luminance changing, objects camouflage, etc. This paper is dedicated to a new method of foreground detection employing region of interest analysis. The main idea of the proposed method is processing feature points located in the regions with object movement. The performance of the foreground detection was estimated using ground truth data and F1-Score criterion.

Keywords

Video surveillance Computer vision Region of interest Feature point SURF SIFT 
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References

  1. 1.
    The Zettabyte Era - Trends and Analysis - Cisco: White Paper. http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/vni-hyperconnectivity-wp.html. Accessed 30 May 2017
  2. 2.
  3. 3.
    Bouwmans, T., Porikli, F., Hoferlin, B., Vacavant, A.: Background Modeling and Foreground Detection for Video Surveillance. Taylor and Francis Group, LLC, Abingdon (2015)MATHGoogle Scholar
  4. 4.
    Gilmutdinov, M., Ustyuzhanin, N.: Analysis of background modeling methods performance in lossy video compression systems. In: Vishnevskiy, V.M., Samouylov, K.E. (eds.) DCCN-2016. CCIS, vol. 628, pp. 481–489. Springer, Heidelberg (2016)Google Scholar
  5. 5.
    Gilmutdinov, M., Nemcev, N.: Approach for foreground detection based on parallax effect analysis, Saint Petersburg State University of Aerospace Instrumentation (2016)Google Scholar
  6. 6.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: The proceedings of the seventh IEEE international conference on Computer Vision (1999)Google Scholar
  7. 7.
    Bay, H., Tuytelaars, T., Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). doi: 10.1007/11744023_32 CrossRefGoogle Scholar
  8. 8.
  9. 9.
  10. 10.
  11. 11.
  12. 12.
  13. 13.
    Olaode, A.A., Naghdy, G., Todd, C.A.: Unsupervised region of interest detection using fast and surf. Comput. Sci. Inf. Technol. 5, 63–72 (2015)Google Scholar
  14. 14.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2001. IEEE (2001)Google Scholar
  15. 15.
    Stevenson, R., Arce, G.: Morphological filters: statistics and further syntactic properties. IEEE Trans. Circ. Syst. 34, 1292–1305 (1987)CrossRefGoogle Scholar
  16. 16.
    Powers, D.M.: Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. J. Mach. Learn. Technol. 2, 37–63 (2011)Google Scholar
  17. 17.
    Stauffer, C., Grimson, W.: Adaptive background mixture models for real - time tracking. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (1999)Google Scholar
  18. 18.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  19. 19.
    Brown, M., et al.: Recognising panoramas. In: ICCV (2003)Google Scholar
  20. 20.
    Delponte, E., et al.: SVD-matching using SIFT features. Graph. Models 68(5), 415–431 (2006)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Saint Petersburg State University of Aerospace Instrumentation (SUAI)Saint-PetersburgRussia
  2. 2.Skolkovo Innovation Center, Skolkovo Institute of Science and TechnologyMoscowRussia

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