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Multimedia Tools and Applications

, Volume 51, Issue 2, pp 697–721 | Cite as

Effective multimedia surveillance using a human-centric approach

  • Pradeep K. AtreyEmail author
  • Abdulmotaleb El Saddik
  • Mohan S. Kankanhalli
Article

Abstract

Large-scale multimedia surveillance installations usually consist of a number of spatially distributed video cameras that are installed in a premise and are connected to a central control station, where human operators (e.g., security personnel) remotely monitor the scene images captured by the cameras. In the majority of these systems the ratio of human operators to the number of camera views is very low. This potentially raises the problem that some important events may be missed. Studies have shown that a human operator can effectively monitor only four camera views. Moreover, the visual attention of human operator drops below the acceptable level while performing the task of visual monitoring. Therefore, there is a need for the selection of the four most relevant camera views at a given time instant. This paper proposes a human-centric approach to solve the problem of dynamically selecting and scheduling the four best camera views. In the proposed approach we use a feedback camera to observe the human monitoring the surveillance camera feeds. Using this information, the system computes the operator’s attention to the camera views to automatically determine the importance of events being captured by the respective cameras. This real-time non-invasive relevance feedback is then augmented with the automatic detection of events to compute the four best feeds. The experiments show the effectiveness of the proposed approach by improving the identification of important events occurring in the environment.

Keywords

Human-centered Multimedia surveillance Relevance feedback Eyes tracking Importance computation Camera view selection and scheduling 

References

  1. 1.
    Amarnag S, Kumaran RS, Gowdy JN (2003) Real time eye tracking for human computer interfaces. In: IEEE international conference on multimedia and expo. Washington DC, USA, pp 557–560Google Scholar
  2. 2.
    Asteriadis S, Tzouveli P, Karpouzis K, Kollias S (2009) Estimation of behavioral user state based on eye gaze and head pose application in an e-learning environment. Multimed Tools Appl 41(3):469–493CrossRefGoogle Scholar
  3. 3.
    Atrey PK (2009) A hierarchical model for representation of events in multimedia observation systems. In: The 1st ACM international workshop on events in multimedia. Beijing, China, pp 57–64Google Scholar
  4. 4.
    Atrey PK, Hossain MA, Saddik AE (2008) Automatic scheduling of cctv camera views using a human-centric approach. In: IEEE international conference on multimedia and expo. Hannover, Germany, pp 325–338Google Scholar
  5. 5.
    Atrey PK, Kankanhalli MS, Jain R (2006) Information assimilation framework for event detection in multimedia surveillance systems. Springer/ACM Multimed Syst J 12(3):239–253CrossRefGoogle Scholar
  6. 6.
    Baumann MA, MacLean KE, Hazelton TW, McKay A (2010) Emulating human attention-getting practices with wearable haptics. In: IEEE haptics symposium. Waltham, USA, pp 149–156CrossRefGoogle Scholar
  7. 7.
    Davis M (2003) Active capture: integrating human-computer interaction and computer vision/audition to automate media capture. In: IEEE international conference on multimedia and expo, vol 2, pp 185–188Google Scholar
  8. 8.
    Dee HM, Velastin SA (2007) How close are we to solving the problem of automated visual surveillance: a review of real-world surveillance, scientific progress and evaluative mechanisms. Mach Vis Appl 19(5–6):329–343zbMATHGoogle Scholar
  9. 9.
    Hampapur A, Brown L, Connell J, Ekin A, Haas N, Lu M, Merkl H, Pankanti S, Senior A, Shu CF, Tian YL (2005) Smart video surveillance: exploring the concept of multiscale spatiotemporal tracking. IEEE Signal Process Mag 22(2):38–51CrossRefGoogle Scholar
  10. 10.
    Hossain MA, Atrey PK, Saddik, AE (2011) Modeling and assessing quality of information in multi-sensor multimedia monitoring systems. ACM Trans Multimed Comput Commun Appl 7(1)Google Scholar
  11. 11.
    Itti L, Baldi P (2009) Bayesian surprise attracts human attention. Vision Res 49(10):1295–1306CrossRefGoogle Scholar
  12. 12.
    Itti L, Koch C (2001) Computational modelling of visual attention. Nat Rev Neurosci 2:194–203CrossRefGoogle Scholar
  13. 13.
    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. Anchorage, USA, pp 1–6Google Scholar
  14. 14.
    Liu A, Zhang Y, Song Y, Zhang D, Li J, Yang Z (2008) Human attention model for semantic scene analysis in movies. In: IEEE international conference on multimedia and expo. Hannover, Germany, pp 1473–1476Google Scholar
  15. 15.
    Ma YF, Lu L, Zhang HJ, Li M (2002) A user attention model for video summarization. In: ACM international conference on multimedia, pp 533–542Google Scholar
  16. 16.
    Menezes P, Barreto JC, Dias J (2004) Face tracking based on haar-like features and eigenfaces. In: The 5th symposium on intelligent autonomous vehicles, pp 5–7Google Scholar
  17. 17.
    Peters C, O’Sullivan C (2003) Attention-driven eye gaze and blinking for virtual humans. In: ACM SIGGRAPH 2003 sketches & applications. San Diego, USA, pp 1–1Google Scholar
  18. 18.
    Radke RJ, Andra S, Al-Kofahi O, Roysam B (2005) Image change detection algorithms: a systematic survey. IEEE Trans Image Process 14(3):294–307CrossRefMathSciNetGoogle Scholar
  19. 19.
    Reinders M (1997) Eye tracking by template matching using an automatic codebook generation scheme. In: Third annual conference of the advanced school for computing and imaging. Heijen, The Netherlands, pp 85–91Google Scholar
  20. 20.
    Rowe LA, Jain R (2005) ACM SIGMM retreat report on future directions in multimedia research. ACM Trans Multimed Comput Commun Appl 1(1):3–13CrossRefGoogle Scholar
  21. 21.
    Savas Z (2005) Real-time detection and tracking of human eyes in video sequences. MSc thesis, Middle East Technical University, Ankara, TurkeyGoogle Scholar
  22. 22.
    Savas Z (2008) Trackeye: real-time tracking of human eyes using a webcam. http://www.codeproject.com/KB/cpp/TrackEye.aspx
  23. 23.
    Smith P, Shah M, da Vitoria Lobo N (2000) Monitoring head/eye motion for driver alertness with one camera. In: IEEE international conference on pattern recognition. Barcelona, Spain, pp 636–642Google Scholar
  24. 24.
    Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: IEEE Computer Society conference on computer vision and pattern recognition, vol 2. Ft. Collins, CO, USA, pp 252–258Google Scholar
  25. 25.
    Taylor JG, Fragopanagos N (2004) Modelling human attention and emotions. In: IEEE international joint conference on neural networks, vol 1. Budapest, Hungary, pp 501–506Google Scholar
  26. 26.
    Vaiapury K, Kankanhalli M (2008) Finding interesting images in albums using attention. J Multimedia 3(4):1–12Google Scholar
  27. 27.
    Vilaplana V, Marques F (2008) Region-based mean shift tracking: application to face tracking. In: The 15th IEEE international conference on image processing. San Diego, CA, pp 2712–2715Google Scholar
  28. 28.
    Vural U, Akgul YS (2009) Eye-gaze based real-time surveillance video synopsis. Pattern Recogn Lett 30:1151–1159CrossRefGoogle Scholar
  29. 29.
    Wallace E, Diffey C (1988) CCTV control room ergonomics. Tech. rep., Police Scientific Development Branch, UK Home OfficeGoogle Scholar
  30. 30.
    Wang J, Kankanhalli MS, Yan W, Jain R (2003) Experiential sampling for video surveillance. In: First ACM international workshop on video surveillance. Berkeley, California, USA, pp 77–86Google Scholar
  31. 31.
    Wu C, Lin Y, Zhang WJ (2005) Human attention modeling in a human-machine interface based on the incorporation of contextual features in a Bayesian network. In: IEEE international conference on systems, man and cybernetics, vol 1. San Antonio, USA, pp 760–766Google Scholar
  32. 32.
    Wu J, Trivedi MM (2010) An eye localization, tracking and blink pattern recognition system: algorithm and evaluation. ACM Trans Multimed Comput Commun Appl 6(2):1–23CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  • Pradeep K. Atrey
    • 1
    Email author
  • Abdulmotaleb El Saddik
    • 2
  • Mohan S. Kankanhalli
    • 3
  1. 1.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  2. 2.Multimedia Communications Research LaboratoryUniversity of OttawaOttawaCanada
  3. 3.School of ComputingNational University of SingaporeKent RidgeSingapore

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