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Multimedia Tools and Applications

, Volume 73, Issue 1, pp 219–240 | Cite as

Utility based decision support engine for camera view selection in multimedia surveillance systems

  • Dewan Tanvir AhmedEmail author
  • M. Anwar Hossain
  • Shervin Shirmohammadi
  • Abdullah AlGhamdi
  • Pradeep K. Atrey
  • Abdulmotaleb El Saddik
Article

Abstract

Design and implementation of an effective surveillance system is a challenging task. In practice, a large number of CCTV cameras are installed to prevent illegal and unacceptable activities where a human operator observes different camera views and identifies various alarming cases. But reliance on the human operator for real-time response can be expensive as he may be unable to pay full attention to all camera views at the same time. Moreover, the complexity of a situation may not be easily perceivable by the operator for which he might require additional support in response to an adverse situation. In this paper, we present a Decision Support Engine (DSE) to select and schedule most appropriate camera views that can help the operator to take an informed decision. For this purpose, we devise a utility based approach where the utility value changes based on automatically detected events in different surveillance zones, event co-relation, and operator’s feedback. In addition to the selected camera views, we propose to synthetically embed extra information around the camera views such as event summary and suggested action plan to globally perceive the current situation. The experimental results show the usefulness of the proposed decision support system.

Keywords

Surveillance system Decision support engine Utility Sensor Feedback Human-computer interaction 

Notes

Acknowledgements

This research is supported by NPST program by King Saud University Project Number 11-INF1830-02.

References

  1. 1.
    Ahmed DT, Shirmohammadi S (2011) A decision support engine for video surveillance systems. In: Proc. IEEE workshop on advances in automated multimedia surveillance for public safety, In: Proc. IEEE international conference on multimedia & Expo, pp 1–6Google Scholar
  2. 2.
    Atrey PK, Kankanhalli M, Jain R (2006) Information assimilation framework for event detection in multimedia surveillance systems. In: Special issue on multimedia surveillance systems in springer/acm multimedia systems journal, pp 239–253Google Scholar
  3. 3.
    Atrey PK, El Saddik A, KankanhalliM(2011) Effective multimedia surveillance using a humancentric approach. Multimedia Tools and Applications 51:697–721CrossRefGoogle Scholar
  4. 4.
    Baumann M, MacLean K, Hazelton T, McKay A (2010) Emulating human attention getting practices with wearable haptics. In: IEEE haptics symposium, pp 149–156Google Scholar
  5. 5.
    Davis M (2003) Active capture: integrating human-computer interaction and computer vision/audition to automate media capture. In: Proc. IEEE international conference on multimedia & expo, vol 2, pp 185–188Google Scholar
  6. 6.
    Hossain MA, Atrey PK, El Saddik A (2011) Modeling and assessing quality of information in multisensor multimedia monitoring systems. ACM Trans Multimedia Comput Commun Appl 7(3):1–3:30CrossRefGoogle Scholar
  7. 7.
    Ivanov Y, Bobick A (2000) Recognition of visual activities and interactions by stochastic parsing. IEEE Trans Pattern Anal Mach Intell 22:852–872CrossRefGoogle Scholar
  8. 8.
    Leykin A, Hammoud R (2008) Real-time estimation of human attention field in lwir and color surveillance videos. In: IEEE international workshop on object tracking and classification in and beyond the visible spectrum, pp 1–6Google Scholar
  9. 9.
    Liu A, Zhang Y, Song Y, Zhang D, Li J, Yang Z (2008) Human attention model for semantic scene analysis in movies. In: Proc. IEEE international conference on multimedia & expo, pp 1473–1476Google Scholar
  10. 10.
    Ma YF, Lu L, Zhang HJ, Li M (2003) A user attention model for video summarization. In: ACM international conference on multimedia, p 533542Google Scholar
  11. 11.
    Nguyen N, Bui H, Venkatesh S, West G (2003) Recognising and monitoring high-level behaviours in complex spatial environments. IEEE Comput Soc Conf Comput Vis Pattern Recogn 2:620Google Scholar
  12. 12.
    Norris C, Armstrong G (1999) The maximum surveillance society: the rise of CCTV. Berg PublishersGoogle Scholar
  13. 13.
    Oates T, Schmill M, Cohen P (2000) A method for clustering the experiences of a mobile robot that accords with human judgments. In: Proceedings of IJCAI, pp 846–851Google Scholar
  14. 14.
    Peters C (2003) Attention-driven eye gaze and blinking for virtual humansGoogle Scholar
  15. 15.
    Rath T, Manmatha R (2003) Features for word spotting in historical manuscripts. In: International conference on document analysis and recognition, vol 1, p 218Google Scholar
  16. 16.
    Smith GJ (2004) Behind the screens: Examining constructions of deviance and informal practices among cctv control room operators in the UK. Surveillance & Society, CCTV Special (eds. Norris, McCahill and Wood) 2(3):376–395Google Scholar
  17. 17.
    Vaiapury K, Kankanhalli MS (2008) Finding interesting images in albums using attention. J Multimed 3:1–12CrossRefGoogle Scholar
  18. 18.
    Wallace E, Diffey C (1998) CCTV control room ergonomics. Tech. rep., police scientific development branch, UK home officeGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2012

Authors and Affiliations

  • Dewan Tanvir Ahmed
    • 1
    Email author
  • M. Anwar Hossain
    • 1
  • Shervin Shirmohammadi
    • 3
  • Abdullah AlGhamdi
    • 1
  • Pradeep K. Atrey
    • 2
  • Abdulmotaleb El Saddik
    • 3
  1. 1.College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  3. 3.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada

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