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Reseach Article

Dominant Flow based Attribute Grouping for Indifferent Movement Detection in Crowd

by Nitish Kumar, Abhishek Vaish
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 88 - Number 18
Year of Publication: 2014
Authors: Nitish Kumar, Abhishek Vaish
10.5120/15449-3790

Nitish Kumar, Abhishek Vaish . Dominant Flow based Attribute Grouping for Indifferent Movement Detection in Crowd. International Journal of Computer Applications. 88, 18 ( February 2014), 1-6. DOI=10.5120/15449-3790

@article{ 10.5120/15449-3790,
author = { Nitish Kumar, Abhishek Vaish },
title = { Dominant Flow based Attribute Grouping for Indifferent Movement Detection in Crowd },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 88 },
number = { 18 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume88/number18/15449-3790/ },
doi = { 10.5120/15449-3790 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:07:55.590333+05:30
%A Nitish Kumar
%A Abhishek Vaish
%T Dominant Flow based Attribute Grouping for Indifferent Movement Detection in Crowd
%J International Journal of Computer Applications
%@ 0975-8887
%V 88
%N 18
%P 1-6
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The growth of technology is heading security systems in a challenging way with amalgamation of different threats and vulnerabilities. Although it is becoming easier day by day to find out the abnormality in the ongoing video with advancement of technology in the field of video cameras, however it is still challenging to detect the undesired event at the time of happening considering the crowd movement in a closed environment. In the following research-paper, we propose a model which proves to be useful and applicable in detection of movement of structured crowd frame by frame in natural sequences. Evaluation results establish the fact that the proposed system is enough capable of detecting the anomalous activities such as merging, splitting or colliding at a point in a certain time than other existing techniques.

References
  1. F. M. Porikli, Trajectory Pattern Detection by HMM Parameter Space feature and Eigenvector Clustering ECCV, 2004.
  2. M. Hu, S. Ali and M. Shah, Detecting Global Motion Pattern in Complex Videos, ICPR, 2008.
  3. M. Rodnguez, S. Ali and T. Kanade, Tracking in Unstructured Crowded Scene ICCV-2009.
  4. M. Hu, S. Ali, and M. Shah, Detecting Global Motion Pattern in Video, ICPR, 2008.
  5. T. B. Moeslund, A. Hilton and V. Kruger, A survey of advances in vision-based human motion capture and analysis.
  6. E. K. Tzamali, M. D. Plumbley and S. A. Velastin Motion estimation for crowd analysis using a hierarchical Bayesian approach, London, U. K.
  7. X. Wong et al. , Learning Semantic Scene Models by Trajectory Analysis ECCV-2006.
  8. R. Mehran, A. Oyama, and M. Shah, "Abnormal crowd behavior detection using Social force Model" in 2009, IEEE (Conference on Computer Vision & Pattern Recognition-2007, pp-1-6.
  9. N. Ihaddadene, and C. Djeraba, "Real time Crowd Motion Analysis" in ICPR 2008, 2008, pp. 1-4.
  10. L. Kratz and K. Nishino, "Anomaly detection in extremely crowded scene using spatio-temporal motion pattern models", in a CVPR, 2009, pp. 1446-1453.
  11. E. Andrade and R. Fisher, "Modeling crowd scene for event detection", Proc. of 18th International Conference on Pattern Recognition ICPR-06 Hong-Kong August 20-24, 2006, pp. 175-178.
  12. M. Black and D. Fleet. Probabilistic detection and tracking of motion boundaries. Int'l. J. Comp. Vision, 38(3):231–245, 2000.
  13. James Hays (Brown); Silvio Savarese, Motion Estimation, (U. of Michigan); Octavia Camps (Northeastern), Web address http://cronos. rutgers. edu/~meer/UGRAD/cv15motion. pdf accessed at 12/01/2014, 1400 hrs.
  14. CAIVIAR dataset, EC Funded CAVIAR project/IST 2001 37540, found at URL: http://homepages. inf. ed. ac. uk/rbf/CAVIAR/
  15. P. Allain, N. Courty, and T. Corpetti. Crowd flow characterization with optimal control theory. In ACCV, Xi'an, China, 2009.
  16. Ozturk, O. , Yamasaki, T. , Aizawa, K. : Detecting dominant motion flows in unstructured/structured crowd scenes. In: Proceedings of the 2010 20th International Conference on Pattern Recognition, ICPR 2010, pp. 3533–3536. IEEE Computer Society, Washington, DC (2010).
  17. Motion Analysis and Object Tracking Using Lucas Kanade Method, accessed at http://docs. opencv. org/master/modules/video/doc/motion_analysis_and_object_tracking. html#motion-analysis-and-object-tracking on 14 Jan, 2014 at 1600 Hrs.
Index Terms

Computer Science
Information Sciences

Keywords

Optical Flow Crowd Movement Anomaly Detection