Real Time Door Access Event Detection and Notification in a Reactive Smart Surveillance System

  • Gaetano Di Caterina
  • Nurulfajar Abd Manap
  • Masrullizam Mat Ibrahim
  • John J. Soraghan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7340)


The effectiveness of modern video surveillance systems critically depends on camera image quality and human operators’ reactivity. In this paper we present a door access event detection application in the context of a reactive smart surveillance system, which automatically notifies in real time the occurrence of events to registered users, through SMS alerts. The system utilizes two fixed IP cameras and a high resolution PTZ camera to acquire high quality images of the face of people entering the room. System users can access a web-based interface to review the event details, along with a short video clip and the high quality face images acquired. Experimental results demonstrate that the final system allows the PTZ camera to automatically acquire high-resolution images of faces and deliver them to system operators in real time.


Face Detection Stereo Match Binary Mask Epipolar Line Short Video Clip 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Valera, A., Velastin, S.A.: Intelligent distributed surveillance systems: a review. IEE Proceedings - Vision, Image and Signal Processing 152, 192–204 (2005)CrossRefGoogle Scholar
  2. 2.
    Di Caterina, G., Soraghan, J.J.: An abandoned and removed object detection algorithm in a reactive smart surveillance system. In: DSP 2011 (2011)Google Scholar
  3. 3.
    Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. Journal of Computer Vision 47, 7–42 (2002)zbMATHCrossRefGoogle Scholar
  4. 4.
    Manap, N.A., Di Caterina, G., Soraghan, J.J., Sidharth, V., Yao, H.: Smart surveillance system based on stereo matching algorithms with IP and PTZ cameras. In: 3DTV-Con 2010, pp. 1–4 (2010)Google Scholar
  5. 5.
    Yang, X., Tian, Y.: Robust door detection in unfamiliar environments by combining edge and corner features. In: IEEE CVPR Workshops, pp. 57–64 (2010)Google Scholar
  6. 6.
    Chaves-Gonzalez, J.M., Vega-Rodriguez, M.A., Gomez-Pulido, J.A., Sanchez-Perez, J.M.: Detecting skin in face recognition systems: a colour spaces study. Digital Signal Processing 20, 806–823 (2010)CrossRefGoogle Scholar
  7. 7.
    Kovac, J., Peer, P., Solina, F.: Human skin color clustering for face detection. In: IEUROCON 2003 - Computer as a Tool, pp. 144–148 (2003)Google Scholar
  8. 8.
    Pai, Y.T., Ruan, S.J., Shie, M.C., Liu, Y.C.: A simple and accurate color face detection algorithm in complex background. In: IEEE ICME, pp. 1545–1548 (2006)Google Scholar
  9. 9.
    Kakumanu, P., Makrogiannis, S., Bourbakis, N.: A survey of skin-color modeling and detection methods. Pattern Recognition 40, 1106–1122 (2007)zbMATHCrossRefGoogle Scholar
  10. 10.
    Bovik, A.: Handbook of image and video processing, 2nd edn. Elsevier, Academic Press (2005)Google Scholar
  11. 11.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Gaetano Di Caterina
    • 1
  • Nurulfajar Abd Manap
    • 1
  • Masrullizam Mat Ibrahim
    • 1
  • John J. Soraghan
    • 1
  1. 1.Department of Electronic and Electrical EngineeringUniversity of StrathclydeGlasgowUK

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