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

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Internet of Things, Smart Spaces, and Next Generation Networks and Systems (ruSMART 2017, NsCC 2017, NEW2AN 2017)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 10531))

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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.

The research of Nikita Ustyuzhanin was partially supported by FASIE.

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References

  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. Visual Networking Index – Cisco. http://www.cisco.com/c/en/us/solutions/service-provider/visual-networking-index-vni/index.html. Accessed 30 May 2017

  3. Bouwmans, T., Porikli, F., Hoferlin, B., Vacavant, A.: Background Modeling and Foreground Detection for Video Surveillance. Taylor and Francis Group, LLC, Abingdon (2015)

    MATH  Google Scholar 

  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. Gilmutdinov, M., Nemcev, N.: Approach for foreground detection based on parallax effect analysis, Saint Petersburg State University of Aerospace Instrumentation (2016)

    Google Scholar 

  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. 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

    Chapter  Google Scholar 

  8. 121 Dataset. http://bmc.iut-auvergne.com/wp-content/ressources/videos/learning/121_png.zip. Accessed 30 May 2017

  9. 111 Dataset. http://bmc.iut-auvergne.com/wp-content/ressources/videos/learning/111_png.zip. Accessed 30 May 2017

  10. Light Switch. http://www.vis.uni-stuttgart.de/hoeferbn/bse/dataset/SABS-LightSwitch.rar. Accessed 30 May 2017

  11. Noisy Night. http://www.vis.uni-stuttgart.de/hoeferbn/bse/dataset/SABS-NoisyNight.rar. Accessed 30 May 2017

  12. Camera Jitter. http://wordpress-jodoin.dmi.usherb.ca/static/dataset/cameraJitter/boulevard.zip. Accessed 30 May 2017

  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. 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. Stevenson, R., Arce, G.: Morphological filters: statistics and further syntactic properties. IEEE Trans. Circ. Syst. 34, 1292–1305 (1987)

    Article  Google Scholar 

  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. 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. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  19. Brown, M., et al.: Recognising panoramas. In: ICCV (2003)

    Google Scholar 

  20. Delponte, E., et al.: SVD-matching using SIFT features. Graph. Models 68(5), 415–431 (2006)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Saint Petersburg State University of Aerospace Instrumentation (SUAI), 67, Bolshaya Morskaya str., Saint-Petersburg, 190000, Russia

    Nikita Ustyuzhanin & Marat Gilmutdinov

  2. Skolkovo Innovation Center, Skolkovo Institute of Science and Technology, Building 3, Moscow, 143026, Russia

    Nikita Ustyuzhanin

Authors
  1. Nikita Ustyuzhanin
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  2. Marat Gilmutdinov
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Corresponding author

Correspondence to Nikita Ustyuzhanin .

Editor information

Editors and Affiliations

  1. Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland

    Olga Galinina

  2. Electronics and Communication Engineering, Tampere University of Technology, Tampere, Finland

    Sergey Andreev

  3. FRUCT Oy, Helsinki, Finland

    Sergey Balandin

  4. Tampere University of Technology, Tampere, Finland

    Yevgeni Koucheryavy

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Ustyuzhanin, N., Gilmutdinov, M. (2017). Foreground Detection Using Region of Interest Analysis Based on Feature Points Processing. In: Galinina, O., Andreev, S., Balandin, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. ruSMART NsCC NEW2AN 2017 2017 2017. Lecture Notes in Computer Science(), vol 10531. Springer, Cham. https://doi.org/10.1007/978-3-319-67380-6_62

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  • DOI: https://doi.org/10.1007/978-3-319-67380-6_62

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67379-0

  • Online ISBN: 978-3-319-67380-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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