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Event Based Switched Dynamic Bayesian Networks for Autonomous Cognitive Crowd Monitoring

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Wide Area Surveillance

Part of the book series: Augmented Vision and Reality ((Augment Vis Real,volume 6))

Abstract

Human behavior analysis is one of the most important applications in Intelligent Video Surveillance (IVS) field. In most recent systems addressed by research, automatic support to the human decisions based on object detection, tracking and situation assessment tools is integrated as a part of a complete cognitive artificial process including security maintenance procedures actions that are in the scope of the system. In such cases an IVS needs to represent complex situations that describe alternative possible real time interactions between the dynamic observed situation and operators’ actions. To obtain such knowledge, particular types of Event based Dynamic Bayesian Networks E-DBNs are here proposed that can switch among alternative Bayesian filtering and control lower level modules to capture adaptive reactions of human operators. It is shown that after the off line learning phase Switched E-DBNs can be used to represent and anticipate possible operators’ actions within the IVS. In this sense acquired knowledge can be used for either fully autonomous security preserving systems or for training of new operators. Results are shown by considering a crowd monitoring application in a critical infrastructure. A system is presented where a Cognitive Node (CN) embedding in a structured way Switched E-DBN knowledge can interact with an active visual simulator of crowd situations. It is also shown that outputs from such a simulator can be easily compared with video signals coming from real cameras and processed by typical Bayesian tracking methods.

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References

  1. Remagnino, P., Velastin, S.A., Foresti, G.L., Trivedi, M.: Novel concepts and challenges for the next generation of video surveillance systems. Mach. Vis. Appl. 18(3), 135–137 (2007)

    Article  Google Scholar 

  2. Trivedi, M., et al.: Intelligent environments and active camera networks. In: Proceedings of the IEEE International Conference on System, Man and Cybernetics, vol. 2, pp. 804–809 (2000)

    Google Scholar 

  3. Lipton, A., Heartwell, C., Haering, N., Madden, D.: Automated video protection, monitoring & detection. IEEE Aerosp. Electron. Syst. Mag. 18(5), 3–18 (2003)

    Article  Google Scholar 

  4. Trivedi, M.M., et al.: Looking-in and looking-out of a vehicle: computer-vision-based enhanced vehicle safety. IEEE Trans. Intell. Transp. Syst. 8(1), 108–120 (2007)

    Google Scholar 

  5. Damasio, A.R.: The Feeling of What Happens-Body, Emotion and the Making of Consciousness. Harvest Books, New York (2000)

    Google Scholar 

  6. Valera, M., Velastin, S.: Intelligent distributed surveillance systems: a review. IEEE Proc. Vis. Image Signal Process. 52(2), 192–204 (2005)

    Article  Google Scholar 

  7. Foresti, G.L., Regazzoni, C.S., Varshney, P.K.: Multisensor Surveillance Systems: The Fusion Perspective. Kluwer Academic, Boston (2003)

    Book  Google Scholar 

  8. Collins, R., Lipton, A., Fujiyoshi, H., Kanade, T.: Algorithms for cooperative multisensory surveillance. Proc. IEEE 89(10), 1456–1477 (2001)

    Article  Google Scholar 

  9. Smith, D., Singh, S.: Approaches to multisensor data fusion in target tracking: a survey. IEEE Trans. Knowl. Data Eng. 18(12), 1696–1710 (2006)

    Article  Google Scholar 

  10. Prati, A., et al.: An integrated multi-modal sensor network for video surveillance. In: Proceedings of the Third ACM International Workshop on Video Surveillance & Sensor Networks (2005)

    Google Scholar 

  11. Chang, B.R., Tsai, H.F., Young, C.-P.: Intelligent data fusion system for predicting vehicle collision warning using vision/gps sensing. Expert. Syst. Appl. 37(3), 2439–2450 (2010)

    Article  Google Scholar 

  12. Wu, S., Decker, S., Chang, P., Camus, T., Eledath, J.: Collision sensing by stereo vision and radar sensor fusion. IEEE Trans. Intell. Transp. Syst. 10(4), 606–614 (2009)

    Article  Google Scholar 

  13. Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.-Q.: Crowd analysis: a survey. Mach. Vis. Appl. 19, 345–357 (2008)

    Article  MATH  Google Scholar 

  14. Loscos, C., Marchal, D., Meyer, A.: Intuitive crowd behavior in dense urban environments using local laws. In: Proceedings of the Theory and Practice of Computer Graphics, pp. 122–129 (2003)

    Google Scholar 

  15. Liu, B., Liu, Z., Hong, Y.: A simulation based on emotions model for virtual human crowds. In: Fifth International Conference on Image and Graphics ICIG’09, pp. 836–840 (2009)

    Google Scholar 

  16. Handford, D., Rogers, A.: Modelling driver interdependent behavior in agent-based traffic simulations for disaster management. In: The Ninth International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 163–172 (2011)

    Google Scholar 

  17. Davies, A.C., Yin, J.H., Velastin, S.A.: Crowd monitoring using image processing. Electron. Commun. Eng. J. 7, 37–47 (1995)

    Article  Google Scholar 

  18. Wren, C.R., Azarbayejani, A., Darrell, T., Pentland, A.P.: Pfinder: real-time tracking of the human body. IEEE Trans. Pattern Anal. Mach. Intell. 19, 780–785 (1997)

    Article  Google Scholar 

  19. Haritaoglu, I., Harwood, D., David, L.S.: W4: real-time surveillance of people and their activities. IEEE Trans. Pattern Anal. Mach. Intell. 22, 809–830 (2000)

    Article  Google Scholar 

  20. Marana, A.N., Velastin, S.A., Costa, L.F., Lotufo, R.A.: Automatic estimation of crowd density using texture. Safety Sci. 28(3), pp. 165–175 (1998)

    Article  Google Scholar 

  21. Zhao, T., Nevatia, R.: Bayesian human segmentation in crowded situations. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, p. 459 (2003)

    Google Scholar 

  22. Andrade, E., Blunsden, S., Fisher, R.: Hidden markov models for optical flow analysis in crowds. In: 18th International Conference on Pattern Recognition.(ICPR), vol. 1, pp. 460–463 (2006)

    Google Scholar 

  23. Benabbas, Y., Ihaddadene, N., Djeraba, C.: Motion pattern extraction and event detection for automatic visual surveillance. EURASIP J. Image Video Process. 2011, 15 (2011)

    Article  Google Scholar 

  24. Rahmalan, H., Nixon, M., Carter, J.: On crowd density estimation for surveillance. In: The Institution of Engineering and Technology Conference on Crime and Security, pp. 540–545 (2006)

    Google Scholar 

  25. Cupillard, F., Avanzi, A., Bremond, F., Thonnat, M.: Video understanding for metro surveillance. In: IEEE International Conference on Networking, Sensing and Control, vol. 1, pp. 186–191 (2004)

    Google Scholar 

  26. Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition CVPR 2009, pp. 935–942 (2009)

    Google Scholar 

  27. Pellegrini, S., Ess, A., Schindler, K., Gool, L. van.: You’ll never walk alone: modeling social behavior for multi-target tracking. In: International Conference on Computer Vision, Miami, 20–26 June 2009

    Google Scholar 

  28. Luber, M., Stork, J.A., Tipaldi, G.D., Arras, K.O.: People tracking with human motion predictions from social forces. In: Proceedings of the International Conference on Robotics & Automation (ICRA), Anchorage (2010)

    Google Scholar 

  29. Moore, B.E., Ali, S., Mehran, R., Shah, M.: Visual crowd surveillance through a hydrodynamics lens. Commun. ACM 54(12), 64–73 (2011)

    Article  Google Scholar 

  30. Dore, A., Regazzoni, C.S.: Bayesian bio-inspired model for learning interactive trajectories. In: Proceedings of the IEEE International Conference on Advanced Video and Signal Based Surveillance, AVSS 2009, Genoa, Italy, 2–4 Sept 2009

    Google Scholar 

  31. Dore, A., Cattoni, A., Regazzoni, C.: Interaction modeling and prediction in smart spaces: a bio-inspired approach based on autobiographical memory. In: IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans (2010)

    Google Scholar 

  32. Kohonen, T.: The self-organizing map. Proc. IEEE 78(9), 1464–1480 (1990)

    Article  Google Scholar 

  33. Dore, A., Soto, M., Regazzoni, C.S.: Bayesian tracking for video analytics. Signal Process. Mag. IEEE. 27(5), pp.46–55, (2010) doi: 10.1109/MSP.2010.937395

    Google Scholar 

  34. Patnaik, D., Laxman, S., Ramakrishnan, N.: Discovering excitatory networks from discrete event streams with applications to neuronal spike train analysis. In: ICDM, pp. 407–416 (2009)

    Google Scholar 

  35. Oliver, N.M., Rosario, B., Pentland, A.P.: A bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000)

    Article  Google Scholar 

  36. Pan, W, Dong, W, Cebrian, M., Kim, T., Fowler, J.H., Pentland, A.S.: Modeling dynamical influence in human interaction: using data to make better inferences about influence within social systems. IEEE Signal Process. Mag. 29(2), 77–86 (2012)

    Google Scholar 

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Authors and Affiliations

  1. Signal Processing and Telecommunications Group, Department of Naval, Electrical, Electronic and Telecommunication Engineering, University of Genoa, Via All’Opera Pia 11A, 16145, Genoa, Italy

    Simone Chiappino, Lucio Marcenaro, Pietro Morerio & Carlo Regazzoni

Authors
  1. Simone Chiappino
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  2. Lucio Marcenaro
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  3. Pietro Morerio
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  4. Carlo Regazzoni
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Corresponding author

Correspondence to Lucio Marcenaro .

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Editors and Affiliations

  1. Electrical and Computer Engineering, University of Dayton, Ohio, USA

    Vijayan K. Asari

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© 2014 Springer-Verlag Berlin Heidelberg

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Chiappino, S., Marcenaro, L., Morerio, P., Regazzoni, C. (2014). Event Based Switched Dynamic Bayesian Networks for Autonomous Cognitive Crowd Monitoring. In: Asari, V. (eds) Wide Area Surveillance. Augmented Vision and Reality, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/8612_2012_8

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  • DOI: https://doi.org/10.1007/8612_2012_8

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37840-9

  • Online ISBN: 978-3-642-37841-6

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