Advertisement

Crowd Monitoring and Classification: A Survey

  • Sonu LambaEmail author
  • Neeta Nain
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 553)

Abstract

Crowd monitoring on public places is very demanding endeavor to accomplish. Huge population and assortment of human actions enforces the crowded scenes to be more continual. Enormous challenges occur into crowd management including proper crowd analysis, identification, monitoring and anomalous activity detection. Due to severe clutter and occlusions, conventional methods for dealing with crowd are not very effective. This paper highlights the various issues involved in analyzing crowd behavior and its dynamics along with classification of crowd analysis techniques. This review summarizes the shortcomings, strength and applicability of existing methods in different environmental scenarios. Furthermore, it overlays the path to device a proficient method of crowd monitoring and classification which can deal with most of the challenges related to this area.

Keywords

Crowd monitoring Behaviour analysis Crowd classification 

References

  1. 1.
    Zhan, Beibei, et al. “Crowd analysis: a survey.” Machine Vision and Applications 19.5–6 (2008): 345–357.Google Scholar
  2. 2.
    Li, Teng, et al. “Crowded scene analysis: A survey.” Circuits and Systems for Video Technology, IEEE Transactions on 25.3 (2015): 367–386.Google Scholar
  3. 3.
    Hu, Weiming, et al. “A survey on visual surveillance of object motion and behaviors.” Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on 34.3 (2004): 334–352.Google Scholar
  4. 4.
    Badal, Tapas, et al. “An Adaptive Codebook Model for Change Detection with Dynamic Background.” 2015 11th International Conference on Signal-Image Technology and Internet Based Systems (SITIS). IEEE, 2015.Google Scholar
  5. 5.
    Leggett, Richard. Real-time crowd simulation: A review. R. Leggett, 2004.Google Scholar
  6. 6.
    Hu, Min, Saad Ali, and Mubarak Shah. “Detecting global motion patterns in complex videos.” Pattern Recognition, 2008. ICPR 2008. 19th International Conference on. IEEE, 2008.Google Scholar
  7. 7.
    Wang, Xiaofei, et al. “A high accuracy flow segmentation method in crowded scenes based on streakline.” Optik-International Journal for Light and Electron Optics 125.3 (2014): 924–929.Google Scholar
  8. 8.
    Mehran, Ramin, Akira Oyama, and Mubarak Shah. “Abnormal crowd behavior detection using social force model.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.Google Scholar
  9. 9.
    Kratz, Louis, and Ko Nishino. “Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes.” Pattern Analysis and Machine Intelligence, IEEE Transactions on 34.5 (2012): 987–1002.Google Scholar
  10. 10.
    Kratz, Louis, and Ko Nishino. “Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models.” Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. IEEE, 2009.Google Scholar
  11. 11.
    Cong, Yang, Junsong Yuan, and Ji Liu. “Abnormal event detection in crowded scenes using sparse representation.” Pattern Recognition 46.7 (2013): 1851–1864.Google Scholar
  12. 12.
    Zhou, Bolei, Xiaogang Wang, and Xiaoou Tang. “Random _eld topic model for semantic region analysis in crowded scenes from tracklets.” Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011.Google Scholar
  13. 13.
    Bak, Slawomir, et al. “Multi-target tracking by discriminative analysis on Riemannian manifold.” Image Processing (ICIP), 2012 19th IEEE International Conference on. IEEE, 2012.Google Scholar
  14. 14.
    Kuo, Cheng-Hao, Chang Huang, and Ramakant Nevatia. “Multi-target tracking by on-line learned discriminative appearance models.” Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on. IEEE, 2010.Google Scholar
  15. 15.
    Badal, Tapas, Neeta Nain, and Mushtaq Ahmed. “Video partitioning by segmenting moving object trajectories.” Seventh International Conference on Machine Vision (ICMV 2014). International Society for Optics and Photonics, 2015.Google Scholar
  16. 16.
    Fruin, John J. Pedestrian planning and design. No. 206 pp. 1971.Google Scholar
  17. 17.
    Chen, Ke, et al. “Feature Mining for Localised Crowd Counting.” BMVC. Vol. 1. No. 2. 2012.Google Scholar
  18. 18.
    Bansal, Ankan, and K. S. Venkatesh. “People Counting in High Density Crowds from Still Images.” arXiv preprint arXiv:1507.08445 (2015).
  19. 19.
    Hussain, Norhaida, et al. “CDES: A pixel-based crowd density estimation system for Masjid al-Haram.” Safety Science 49.6 (2011): 824–833.Google Scholar
  20. 20.
    Tian, Qing, et al. “Human detection using HOG features of head and shoulder based on depth map.” Journal of Software 8.9 (2013): 2223–2230.Google Scholar
  21. 21.
    Idrees, Haroon, et al. “Multi-source multi-scale counting in extremely dense crowd images.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013.Google Scholar
  22. 22.
    Conte, Donatello, et al. “A method for counting moving people in video surveillance videos.” EURASIP Journal on Advances in Signal Processing 2010 (2010).Google Scholar
  23. 23.
    Albiol, A., Silla, M.J., Albiol, A., Mossi, J.M., 2009. Video analysis using corner motion statistics. In: IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, pp. 31–38.Google Scholar
  24. 24.
    Xi, Wei, et al. “Electronic frog eye: Counting crowd using WiFi.” INFOCOM, 2014 Proceedings IEEE. IEEE, 2014.Google Scholar
  25. 25.
    Luo, Jun, et al. “Real-time people counting for indoor scenes.” Signal Processing (2015).Google Scholar
  26. 26.
    Fradi, Hajer, and Jean-Luc Dugelay. “Towards crowd density-aware video surveillance applications.” Information Fusion 24 (2015): 3–15.Google Scholar
  27. 27.
    Lebano_, Logan, and Haroon Idrees. “Counting in Dense Crowds using Deep Learning”.Google Scholar
  28. 28.
    Zhang, Cong, et al. “Cross-scene crowd counting via deep convolutional neural networks.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.Google Scholar
  29. 29.
    Rodriguez, Mikel, et al. “Density-aware person detection and tracking in crowds.” Computer Vision (ICCV), 2011 IEEE International Conference on. IEEE, 2011.Google Scholar
  30. 30.
    Andrade, Ernesto L., Scott Blunsden, and Robert B. Fisher. “Modeling crowd scenes for event detection.” Pattern Recognition, 2006. ICPR 2006. 18th International Conference on. Vol. 1. IEEE, 2006.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2017

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringMNITJaipurIndia

Personalised recommendations