Advertisement

CCTV Video Analytics: Recent Advances and Limitations

  • Sergio A. Velastin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5857)

Abstract

There has been a significant increase in the number of CCTV cameras in public and private places worldwide. The cost of monitoring these cameras manually and of reviewing recorded video is prohibitive and therefore manual systems tend to be used mainly reactively with only a small fraction of the cameras being monitored at any given time. There is a need to automate at least simple observation tasks through computer vision, a functionality that has become known popularly as “video analytics”. The large size of CCTV systems and the requirement of high detection rates and low false alarms are major challenges. This paper illustrates some of the recent efforts reported in the literature, highlighting advances and pointing out important limitations.

Keywords

Closed-circuit television video analytics visual surveillance image processing security crowd-monitoring tracking object detection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    CCTV Users Group: How Many Cameras are there? 2008-06-18 (2008), http://www.cctvusergroup.com/art.php?art=94
  2. 2.
    Kingston University, Mott MacDonald, Ipsotek Limited: Maximising benefits from CCTV on the railway - Existing Systems, Technical Report, Rail Safety and Standards Board (2003)Google Scholar
  3. 3.
    Dee, H., Velastin, S.A.: How Close are we to Solving the Problem of Automated Visual Surveillance? A Review of Real-World Surveillance. Scientific Progress and Evaluative Mechanisms; Machine Vision and Applications 19(5-6), 329–343 (2008)CrossRefGoogle Scholar
  4. 4.
    Whitehead, C.M.E., Stockdale, J.E., Razzu, G.: The Economic and Social Cost of Anti-Social Behaviour: A Review, London School of Economics and Political Science (October 2003), http://www.homeoffice.gov.uk/crimpol/antisocialbehaviour
  5. 5.
    DTLR, based on a report by Crime Concern and Oscar Faber (ref. SP/15 and SP/16) (2002)Google Scholar
  6. 6.
    Velastin, S.A., Boghossian, B.A., Vicencio-Silva, M.A.: A Motion-Based Image Processing System for Detecting Potentially Dangerous Situations in Underground Railway Stations. Transportation Research Part C: Emerging Technologies 14(2), 96–113 (2006)CrossRefGoogle Scholar
  7. 7.
    http://www.cvg.rdg.ac.uk/PETS2009/ (accessed 18, August 2009 )
  8. 8.
  9. 9.
    Yin, F., Makris, D., Velastin, S.A.: Performance Evaluation of Object Tracking Algorithms. In: 10th IEEE Int. Workshop on Performance Evaluation of Tracking and Surveillance (PETS-2007), Rio de Janeiro, Brazil (2007)Google Scholar
  10. 10.
    Stauffer, C., Grimson, E.: Learning Patterns of Activity Using Real-Time Tracking. IEEE TPAMI 22(8), 747–757 (2000)Google Scholar
  11. 11.
    Yin, Y., Makris, D., Velastin, S.A.: Time Efficient Ghost Removal for Motion Detection in Visual Surveillance Systems. IET Electronics Letters 44(23), 1351–1353 (2008)CrossRefGoogle Scholar
  12. 12.
    Cucchiara, R., Grana, C., Piccardi, M., Prati, A.: Detecting Moving Objects, Ghosts, and Shadows in Video Streams. IEEE TPAMI 25/10, 1337–1342 (2003)Google Scholar
  13. 13.
    Martel-Brisson, N., Zaccarin, A.: Learning and Removing Cast Shadows through a Multidistribution Approach. IEEE TPAMI 29(7), 1133–1146 (2007)Google Scholar
  14. 14.
    Yilmaz, A., Javed, O., Shah, M.: Object Tracking: a Survey, ACM Computing Surveys, 38/4, 13.1–13.45 (2006)Google Scholar
  15. 15.
    Sigal, L., Black, M.J.: HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Motion, Brown University, Tech Rep CS-06-08 (2006)Google Scholar
  16. 16.
    Martinez-del-Rincon, J., Nebel, J.-C., Makris, D., Orrite- Uruñuela, C.: Tracking Human Body Parts Using Particle Filters Constrained by Human Biomechanics. In: British Machine Vision Conference, Leeds (2008)Google Scholar
  17. 17.
    Ragheb, H., Velastin, S.A., Remagnino, P., Ellis, T.: Novel Approach for Fast Action Recognition using Simple Features. In: 8th IEEE Int. Workshop on Visual Surveillance VS 2008, Marseille, France, October 17 (2008)Google Scholar
  18. 18.
    Ragheb, H., Velastin, S.A., Remagnino, P., Ellis, T.: ViHASi: Virtual Human Action Silhouette Data for the Performance Evaluation of Silhouette-Based Action Recognition Methods. In: Workshop on Activity Monitoring by Multi-Camera Surveillance Systems (ACM/IEEE Int’l. Conf. on Distributed Smart Cameras), September 11, Stanford University, California (2008)Google Scholar
  19. 19.
  20. 20.
    Martinez-Contreras, F., Orrite-Uruñuela, C., Herrero-Jaraba, E., Ragheb, H., Velastin, S.A.: Recognizing Human Actions using Silhouette-based HMM. In: 6th IEEE Int. Conference on Advanced Video and Signal Based Surveillance, AVSS, Genoa, Italy, September 2-4 (2009)Google Scholar
  21. 21.
    Damen, D., Hogg, D.: Associating People Dropping off and Picking up Object. In: Proc. British Machine Vision Conference, BMVC (2007)Google Scholar
  22. 22.
    Damen, D., Hogg, D.: Recognizing Linked Events: Searching the Space of Feasible Explanations. In: Computer Vision and Pattern Recognition, CVPR (2009)Google Scholar
  23. 23.
    Damen, D., Hogg, D.C.: Detecting carried objects in short video sequences. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 154–167. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  24. 24.
    Lv, F., Song, X., Wu, B., Kumar Singh, V., Nevatia, R.: Left-Luggage Detection using Bayesian Inference. In: 9th Int Workshop on Performance Evaluation of Tracking and Surveillance, PETS-CVPR 2006 (June 2006)Google Scholar
  25. 25.
    Wu, B., Nevatia, R.: Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors. Int. J. Computer Vision 75(2), 247–266 (2007)CrossRefGoogle Scholar
  26. 26.
    Huang, C., Wu, B., Nevatia, R.: Robust object tracking by hierarchical association of detection responses. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 788–801. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  27. 27.
    Zhan, B., Monekosso, D.N., Remagnino, P., Velastin, S.A., Xu, L.: Crowd Analysis: a Survey. Machine Vision and Applications. Computer Science, 345–357 (2008) ISBN- 978-3-540-88689-1Google Scholar
  28. 28.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part II. LNCS, vol. 5303, pp. 1–14. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  29. 29.
    Black, J., Ellis, T.: Multi camera image tracking, Image and Vision Computing, vol. 24, pp. 1256–1267. Elsevier, Amsterdam (2006)Google Scholar
  30. 30.
    Makris, D., Ellis, T.: Learning Semantic Scene Models from Observing Activity in Visual Surveillance. IEEE TSMC-B 35(3), 397–408 (2005)Google Scholar
  31. 31.
    Hu, M., Zhou, X., Tan, T., Lou, J., Maybank, S.: Principal Axis-Based Correspondence between Multiple Cameras for People Tracking. IEEE TPAMI 28/4, 663–671 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Sergio A. Velastin
    • 1
  1. 1.Digital Imaging Research Centre, Faculty of Computing, Information Systems and MathematicsKingston UniversityKingston upon ThamesUnited Kingdom

Personalised recommendations