Privacy-preserving and IoT-capable Crowd Analysis and Detection of Flow Disturbances for Enhancing Public Safety

Hans G. Ziegler

2016

Abstract

This paper describes a solution for monitoring and detection of crowds and analysis of density structures and movement characteristics, to enhance safety of citizens and security of critical infrastructures. The system leverages the Internet of Things concept and heterogenous, energy efficient, networked sensors, with support for wireless communication. Privacy protection, instant deployability and auto configuration are hereby underlying core objectives. The solution, which will be described, comprises two novel distributed crowd analysis algorithms, allowing on the one hand the localisation of critical areas within large crowds and on the other hand the recognition of counter streams, which can cause severe impacts on the crowd flow and movement velocity and which can transform crowding scenarios into threatening situations.

References

  1. D. Helbing, I. Farkas, & T. Vicsek. Simulating dynamical features of escape panic. Nature, 407(6803), 487-490, 2000.
  2. E. Baccelli, G. Bartl, A. Danilkina, V. Ebner, F. Gendry, C. Guettier, O. Hahn, U. Kriegel, G. Hege, M. Palkow, H. Petersen, T.C. Schmidt, A. Voisard, M. Whlisch, H. Ziegler. Area & Perimeter Surveillance in SAFEST using Sensors and the Internet of Things. Proceedings of the French Interdisciplinary Workshop on Global Security (WISG), Troyes, France, 2014.
  3. R. Mehran, A. Oyama, and M. Shah. Abnormal crowd behavior detection using social force model. In CVPR, 2009.
  4. D. Helbing, & P. Molnar. Social force model for pedestrian dynamics. Physical review E 51(5), 4282, 1995.
  5. S. P. Hoogendoorn, & W. Daamen. Pedestrian behavior at bottlenecks. Transportation Science, 39(2), 147-159, 2005.
  6. A. Schadschneider, W. Klingsch, H. Klpfel, T. Kretz, C. Rogsch, & A. Seyfried. Evacuation dynamics: Empirical results, modeling and applications. In Encyclopedia of complexity and systems science (pp. 3142-3176). Springer New York. 2009.
  7. B. Krausz & C. Bauckhage. Automatic detection of dangerous motion behavior in human crowds. AVSS, 2011.
  8. B. Krausz, & C. Bauckhage, Loveparade 2010: Automatic video analysis of a crowd disaster. CVIU, p. 307-319, 2012.
  9. M. Ester, H. P. Kriegel, J. Sander & X. Xu. A densitybased algorithm for discovering clusters in large spatial databases with noise. KDD. Vol. 96. 1996.
  10. J.B. MacQueen. ”Some methods for classification and analysis of multivariate observations”. 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281-297. 1967.
  11. M. Ankerst, M. M. Breunig, H. P. Kriegel & J. Sander. Optics: Ordering points to identify the clustering structure. SIGMOD Vol. 28, No. 2, pp. 49-60. 1999.
  12. T.K. Moon.The expectation maximization algorithm. Signalprocessing magazine, 13(6), 47-60. 1996.
  13. V. Eiselein, H. Fradi, I. Keller, T. Sikora & J. L. Dugelay. Enhancing human detection using crowd density measures and an adaptive correction filter. In AVSS, pp. 19-24, 2013.
  14. H. Rahmalan, M. S. Nixon & J. N. Carter, J. N. On crowd density estimation for surveillance. In Crime and Security, IET, pp. 540-545, 2006.
  15. R. Ma, L. Li, W. Huang & Q. Tian. On pixel count based crowd density estimation for visual surveillance. In Cybernetics and Intelligent Systems, Vol. 1, pp. 170- 173. 2004.
  16. V. Mahadevan, W. Li, V. Bhalodia & N. Vasconcelos. Anomaly detection in crowded scenes. In CVPR, pp. 1975-1981, 2010.
  17. E. U. Kriegel, S. Pfennigschmidt & H. G. Ziegler. Practical aspects of the use of a Knowledge Fusion Toolkit in safety applications. In Autonomous Decentralized Systems. ISADS, pp. 1-4, 2013.
  18. C. M. Bishop. Pattern recognition and machine learning, Springer, Singapore, p. 439, 2006.
  19. J. L. Carlson, Redis in Action, Manning Publications, ISBN: 9781617290855, 2013. 26.
  20. D. Helbing, A. Johansson, and H. Z. Al-Abideen. Dynamics of crowd disasters: An empirical study. Physical Review E 75(4):04610917, 2007.
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Paper Citation


in Harvard Style

Ziegler H. (2016). Privacy-preserving and IoT-capable Crowd Analysis and Detection of Flow Disturbances for Enhancing Public Safety . In Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS, ISBN 978-989-758-184-7, pages 55-62. DOI: 10.5220/0005761300550062


in Bibtex Style

@conference{smartgreens16,
author={Hans G. Ziegler},
title={Privacy-preserving and IoT-capable Crowd Analysis and Detection of Flow Disturbances for Enhancing Public Safety},
booktitle={Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,},
year={2016},
pages={55-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005761300550062},
isbn={978-989-758-184-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 5th International Conference on Smart Cities and Green ICT Systems - Volume 1: SMARTGREENS,
TI - Privacy-preserving and IoT-capable Crowd Analysis and Detection of Flow Disturbances for Enhancing Public Safety
SN - 978-989-758-184-7
AU - Ziegler H.
PY - 2016
SP - 55
EP - 62
DO - 10.5220/0005761300550062