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Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems

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Journal of Applied Spectroscopy Aims and scope

Study of crowd movement is an important practical problem, and its solution is used in video surveillance systems for preventing various emergency situations. In the general case, a group of fast-moving people is of more interest than a group of stationary or slow-moving people. We propose a new method for crowd movement analysis using a video sequence, based on integral optical flow. We have determined several characteristics of a moving crowd such as density, speed, direction of motion, symmetry, and in/out index. These characteristics are used for further analysis of a video scene.

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References

  1. J. C. S. Jacques, Jr., S. R. Musse, and C. R. Jung, IEEE Signal Proc. Magaz., 27, No. 5, 66–77 (2010).

    ADS  Google Scholar 

  2. J. Shao, C. Loy, K. Kang, and X. Wang, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 5629–5628.

  3. R. Mehran, A. Oyama, and M. Shah, in: Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2009), pp. 935–942.

  4. S. Ali and M. Shah, in: Proc. IEEE Conference on Computer Vision and Pattern Recognition (2007), pp. 1–6.

  5. B. Solmaz, B. E. Moore, and M. Shah, IEEE Transactions on Pattern Analysis and Machine Intelligence, 34, No. 10, 2064–2070 (2012).

    Article  Google Scholar 

  6. A. Niranjil Kumar and C. Sureshkumar, Int. J. Adv. Res. Comput. Commun. Eng., 3, No. 9, 7935–7939 (2014).

    Google Scholar 

  7. Shipra Ojha and S. R. Sakhare, Int. J. Sci. Eng. Res., 6, No. 6, 1119–1123 (2015).

    Google Scholar 

  8. N. A. Sjarif, S. M. Shamsuddin, and S. Z. Hashim, Int. J. Adv. Software Comput. Appl., 4, No. 1, 1–33 (2012).

    Google Scholar 

  9. B. K. Horn and B. G. Schunck, Artificial Intelligence, 17, Nos. 1–3, 185–203 (1981).

    Article  Google Scholar 

  10. G. Farnebäck, in: Scandinavian Conference on Image Analysis (2003), pp. 363–370.

  11. C. Chen, S. Ye, H. Chen, O. V. Nedzvedz, and S. V. Ablameyko, Zh. Prikl. Spektrosk., 84, No. 1, 138–146 (2017) [C. Chen, S. Ye, H. Chen, O. Nedzvedz, and S. Ablameyko, J. Appl. Spectrosc., 84, No. 1, 120–128 (2017) (English translation)].

  12. P. Allain, N. Courty, and T. Corpetti, in: Proc. First ICPR International Workshop on Pattern Recognition and Crowd Analysis, November 2012 (2012), pp. 1–6.

  13. N. Courty, P. Allain, C. Creusot, and T. Corpetti, Pattern Recognition Lett., 44, No. 8, 161–170 (2014).

    Article  Google Scholar 

  14. https://www.sites.univ-rennes2.fr/costel/corpetti/agoraset/Site/Scenes.html.

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Authors and Affiliations

  1. Zhejiang Shuren University, Hangzhou, 310015, China

    H. Chen & Sh. Ye

  2. Belarusian State Medical University, Minsk, Belarus

    O. V. Nedzvedz

  3. Belarusian State University, 4 Nezavisimost’ Ave., Minsk, 220030, Belarus

    S. V. Ablameyko

  4. The United Institute of Informatics Problems, National Academy of Sciences of Belarus, Minsk, Belarus

    S. V. Ablameyko

Authors
  1. H. Chen
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  2. Sh. Ye
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  3. O. V. Nedzvedz
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  4. S. V. Ablameyko
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Corresponding authors

Correspondence to H. Chen or S. V. Ablameyko.

Additional information

Translated from Zhurnal Prikladnoi Spektroskopii, Vol. 85, No. 1, pp. 135–143, January–February, 2018.

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Chen, H., Ye, S., Nedzvedz, O.V. et al. Application of Integral Optical Flow for Determining Crowd Movement from Video Images Obtained Using Video Surveillance Systems. J Appl Spectrosc 85, 126–133 (2018). https://doi.org/10.1007/s10812-018-0622-8

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  • DOI: https://doi.org/10.1007/s10812-018-0622-8

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