Advertisement

Security and Surveillance

  • Shaogang Gong
  • Chen Change Loy
  • Tao Xiang

Abstract

Human eyes are highly efficient devices for scanning through a large quantity of low-level visual sensory data and delivering selective information to one’s brain for high-level semantic interpretation and gaining situational awareness. Over the last few decades, the computer vision community has endeavoured to bring about similar perceptual capabilities to artificial visual sensors. Substantial efforts have been made towards understanding static images of individual objects and the corresponding processes in the human visual system. This endeavour is intensified further by the need for understanding a massive quantity of video data, with the aim to comprehend multiple entities not only within a single image but also over time across multiple video frames for understanding their spatio-temporal relations. A significant application of video analysis and understanding is intelligent surveillance, which aims to interpret automatically human activity and detect unusual events that could pose a threat to public security and safety.

Keywords

Video Analytic Camera View Unusual Event Visual Context Crowd Behaviour 
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.

References

  1. 1.
    Aghajan, H., Cavallaro, A. (eds.): Multi-camera Networks: Principles and Applications. Elsevier, Amsterdam (2009) Google Scholar
  2. 2.
    Alahia, A., Vandergheynsta, P., Bierlaireb, M., Kunt, M.: Cascade of descriptors to detect and track objects across any network of cameras. Comput. Vis. Image Underst. 114, 624–640 (2010) CrossRefGoogle Scholar
  3. 3.
    Ali, S., Shah, M.: A Lagrangian particle dynamics approach for crowd flow segmentation and stability analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6 (2007) CrossRefGoogle Scholar
  4. 4.
    Ali, S., Shah, M.: Floor fields for tracking in high density crowd scenes. In: European Conference on Computer Vision, pp. 1–24 (2008) Google Scholar
  5. 5.
    Bar, M.: Visual objects in context. Nat. Rev. Neurosci. 5, 617–629 (2004) CrossRefGoogle Scholar
  6. 6.
    Bashir, K., Xiang, T., Gong, S.: Gait recognition without subject cooperation. Pattern Recogn. Lett. 31(13), 2052–2060 (2010) CrossRefGoogle Scholar
  7. 7.
    Benezeth, Y., Jodoin, P.-M., Saligrama, V., Rosenberger, C.: Abnormal events detection based on spatio-temporal co-occurrences. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2458–2465 (2009) CrossRefGoogle Scholar
  8. 8.
    Benfold, B., Reid, I.: Guiding visual surveillance by tracking human attention. In: British Machine Vision Conference (2009) Google Scholar
  9. 9.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003) MATHGoogle Scholar
  10. 10.
    Bosch: Athens International Airport (2001) http://www.boschsecurity.co.uk/
  11. 11.
    Breitenstein, M.D.: Visual surveillance – dynamic behavior analysis at multiple levels. PhD thesis, ETH Zurich (2009) Google Scholar
  12. 12.
    Cao, L., Liu, Z., Huang, T.S.: Cross-dataset action detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1998–2005 (2010) Google Scholar
  13. 13.
    Chen, K.-W., Lai, C.-C., Hung, Y.-P., Chen, C.-S.: An adaptive learning method for target tracking across multiple cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008) CrossRefGoogle Scholar
  14. 14.
    IBM Corporation: Command, Control, Collabo-rate: Public Safety Solutions from IBM. Solution Brief (2009) Google Scholar
  15. 15.
    Ekman, P.: Facial expressions of emotion: New findings, new questions. Psychol. Sci. 3(1), 34–38 (1992) MathSciNetCrossRefGoogle Scholar
  16. 16.
    Ekman, P., Friesen, W.V.: Unmasking the Face, 2nd edn. Consulting Psychologists Press, Palo Alto (1984) Google Scholar
  17. 17.
    Essa, I.A., Pentland, A.P.: Coding, analysis, interpretation, and recognition of facial expressions. IEEE Trans. Pattern Anal. Mach. Intell. 19(7), 757–763 (1997) CrossRefGoogle Scholar
  18. 18.
    Everts, I., Sebe, N., Jones, G.A.: Cooperative object tracking with multiple PTZ cameras. In: International Conference on Image Analysis and Processing, pp. 323–330 (2007) Google Scholar
  19. 19.
    Farenzena, M., Bazzani, L., Perina, A., Cristani, M., Murino, V.: Person re-identification by symmetry-driven accumulation of local features. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2360–2367 (2010) Google Scholar
  20. 20.
    Felzenszwalb, P.F., Huttenlocher, D.P.: Pictorial structures for object recognition. Int. J. Comput. Vis. 61(1), 55–79 (2005) CrossRefGoogle Scholar
  21. 21.
    Frost & Sullivan: Video Surveillance Software Emerges as Key Weapon in Fight Against Terrorism. http://www.frost.com/
  22. 22.
    Frost & Sullivan: Eyes on the Network – Understanding the Shift Toward Network-based Video Surveillance in Asia (2007). http://www.frost.com/prod/servlet/market-insight-top.pag?docid=100416385
  23. 23.
    Gilbert, A., Bowden, R.: Incremental, scalable tracking of objects inter camera. Comput. Vis. Image Underst. 111(1), 43–58 (2008) CrossRefGoogle Scholar
  24. 24.
    Gill, P.M., Spriggs, A., Allen, J., Hemming, M., Jessiman, P., Kara, D., Kilworth, J., Little, R., Swain, D.: Control room operation: findings from control room observations. Home office online report 14/05, Home Office (2005) Google Scholar
  25. 25.
    Gouaillier, V., Fleurant, A.-E.: Intelligent video surveillance: Promises and challenges. Technological and commercial intelligence report, CRIM and Technôpole Defence and Security (2009) Google Scholar
  26. 26.
    Gray, D., Tao, H.: Viewpoint Invariant Pedestrian Recognition with an Ensemble of Localized Features. In: European Conference on Computer Vision, pp. 262–275 (2008) Google Scholar
  27. 27.
    Hongeng, S., Nevatia, R., Brémond, F.: Video-based event recognition: Activity representation and probabilistic recognition methods. Comput. Vis. Image Underst. 96(2), 129–162 (2004) CrossRefGoogle Scholar
  28. 28.
    Hospedales, T., Gong, S., Xiang, T.: A Markov clustering topic model for mining behaviour in video. In: IEEE International Conference on Computer Vision, pp. 1165–1172 (2009) CrossRefGoogle Scholar
  29. 29.
    Hu, Y., Cao, L., Lv, F., Yan, S., Gong, Y., Huang, T.S.: Action detection in complex scenes with spatial and temporal ambiguities. In: IEEE International Conference on Computer Vision (2009) Google Scholar
  30. 30.
    Javed, O., Shafique, K., Shah, M.: Appearance Modeling for Tracking in Multiple Non-overlapping Cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 26–33 (2005) Google Scholar
  31. 31.
    Javed, O., Shah, M.: Automated Multi-camera Surveillance: Theory and Practice. Springer, New York (2008) CrossRefGoogle Scholar
  32. 32.
    Junior, J.C.S.J., Musse, S.R., Jung, C.R.: Crowd analysis using computer vision techniques. IEEE Signal Process. Mag. 27, 66–77 (2010) Google Scholar
  33. 33.
    Ke, Y., Sukthankar, R., Hebert, M.: Event detection in crowded videos. In: IEEE International Conference on Computer Vision, pp. 1–8 (2007) CrossRefGoogle Scholar
  34. 34.
    Kim, J., Grauman, K.: Observe locally, infer globally: A space–time MRF for detecting abnormal activities with incremental updates. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2928 (2009) Google Scholar
  35. 35.
    Kratz, L., Nishino, K.: Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1446–1453 (2009) CrossRefGoogle Scholar
  36. 36.
    Kratz, L., Nishino, K.: Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 693–700 (2010) Google Scholar
  37. 37.
    Kuettel, D., Breitenstein, M.D., Gool, L.V., Ferrari, V.: What’s going on? Discovering spatio-temporal dependencies in dynamic scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1951–1958 (2010) Google Scholar
  38. 38.
    Lavee, G., Rivlin, E., Rudzsky, M.: Understanding video events: A survey of methods for automatic interpretation of semantic occurrences in video. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 39(5), 489–504 (2009) CrossRefGoogle Scholar
  39. 39.
    Li, J., Gong, S., Xiang, T.: Global behaviour inference using probabilistic latent semantic analysis. In: British Machine Vision Conference, pp. 193–202 (2008) Google Scholar
  40. 40.
    Li, J., Gong, S., Xiang, T.: Scene segmentation for behaviour correlation. In: European Conference on Computer Vision, pp. 383–395 (2008) Google Scholar
  41. 41.
    Loy, C.C., Xiang, T., Gong, S.: Stream-based active unusual event detection. In: Asian Conference on Computer Vision (2010) Google Scholar
  42. 42.
    Mahadevan, V., Li, W., Bhalodia, V., Vasconcelos, N.: Anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition (2010) Google Scholar
  43. 43.
    Makris, D., Ellis, T., Black, J.: Bridging the Gaps Between Cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 205–210 (2004) Google Scholar
  44. 44.
    Mehran, R., Oyama, A., Shah, M.: Abnormal crowd behaviour detection using social force model. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 935–942 (2009) CrossRefGoogle Scholar
  45. 45.
    Mehran, R., Moore, B.E., Shah, M.: A streakline representation of flow in crowded scenes. In: European Conference on Computer Vision (2010) Google Scholar
  46. 46.
    Moeslund, T.B., Hilton, A., Krüger, V.: A survey of advances in vision-based human motion capture and analysis. Comput. Vis. Image Underst. 104(2), 90–126 (2006) CrossRefGoogle Scholar
  47. 47.
    Murphy-Chutorian, E., Trivedi, M.M.: Head pose estimation in computer vision: A survey. IEEE Trans. Pattern Anal. Mach. Intell. 31(4), 607–626 (2009) CrossRefGoogle Scholar
  48. 48.
    Nguyen, H.T., Ji, Q., Smeulders, A.W.M.: Spatio-temporal context for robust multitarget tracking. IEEE Trans. Pattern Anal. Mach. Intell. 29(1), 52–64 (2007) CrossRefGoogle Scholar
  49. 49.
    Nixon, M.S., Tan, T., Chellappa, R.: Human Identification Based on Gait. Springer, New York (2005) Google Scholar
  50. 50.
    ObjectVideo: Hardening U.S. Borders (2003). http://www.objectvideo.com/
  51. 51.
    Oliver, N., Rosario, B., Pentland, A.: A Bayesian computer vision system for modeling human interactions. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 831–843 (2000) CrossRefGoogle Scholar
  52. 52.
    Orozco, J., Gong, S., Xiang, T.: Head pose classification in crowded scenes. In: British Machine Vision Conference (2009) Google Scholar
  53. 53.
    Poppe, R.: A survey on vision-based human action recognition. Image Vis. Comput. 28(6), 976–990 (2010) CrossRefGoogle Scholar
  54. 54.
    Prosser, B., Gong, S., Xiang, T.: Multi-camera matching using bi-directional cumulative brightness transfer functions. In: British Machine Vision Conference (2008) Google Scholar
  55. 55.
    Ran, Y., Zheng, Q., Chellappa, R., Strat, T.M.: Applications of a simple characterization of human gait in surveillance. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 40(4), 1009–1020 (2010) CrossRefGoogle Scholar
  56. 56.
    Rodriguez, M., Ali, S., Kanade, T.: Tracking in unstructured crowded scenes. In: IEEE International Conference on Computer Vision (2009) Google Scholar
  57. 57.
    Schneiderman, R.: Trends in video surveillance give DSP an apps boost [special reports]. IEEE Signal Process. Mag. 27(6), 6–12 (2010) Google Scholar
  58. 58.
    Schwartz, O., Hsu, A., Dayan, P.: Space and time in visual context. Nat. Rev., Neurosci. 8, 522–535 (2007) CrossRefGoogle Scholar
  59. 59.
    Settles, B.: Active learning literature survey. Technical report, University of Wisconsin-Madison (2010) Google Scholar
  60. 60.
    Shan, C., Gong, S., McOwan, P.W.: Facial expression recognition based on local binary patterns: A comprehensive study. Image Vis. Comput. 27(6), 803–816 (2009) CrossRefGoogle Scholar
  61. 61.
    Sillito, R.R., Fisher, R.B.: Semi-supervised learning for anomalous trajectory detection. In: British Machine Vision Conference (2008) Google Scholar
  62. 62.
    Siva, P., Xiang, T.: Action detection in crowd. In: British Machine Vision Conference (2010) Google Scholar
  63. 63.
    Team, i.: Imagery library for intelligent detection systems (i-LIDS); a standard for testing video based detection systems. In: Annual IEEE International Carnahan Conferences Security Technology, pp. 75–80 (2006) Google Scholar
  64. 64.
    Teh, Y., Jordan, M., Beal, M., Blei, D.: Hierarchical Dirichlet processes. J. Am. Stat. Assoc. 101(476), 1566–1581 (2006) MathSciNetMATHCrossRefGoogle Scholar
  65. 65.
    Loy, C.C., Xiang, T., Gong, S.: Modelling activity global temporal dependencies using time delayed probabilistic graphical model. In: IEEE International Conference on Computer Vision, pp. 120–127 (2009) CrossRefGoogle Scholar
  66. 66.
    Loy, C.C., Xiang, T., Gong, S.: Time-delayed correlation analysis for multi-camera activity understanding. Int. J. Comput. Vis. 90(1), 106–129 (2010) CrossRefGoogle Scholar
  67. 67.
    Tian, Y.-L., Kanade, T., Cohn, J.F.: Facial Expression Analysis. Springer, New York (2005). Chap. 11 Google Scholar
  68. 68.
    Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: IEEE International Conference on Computer Vision, pp. 1842–1849 (2005) Google Scholar
  69. 69.
    Turaga, P., Chellappa, R., Subrahmanian, V.S., Udrea, O.: Machine recognition of human activities – A survey. IEEE Trans. Circuits Syst. Video Technol. 18(11), 1473–1488 (2008) CrossRefGoogle Scholar
  70. 70.
    Wang, X., Ma, X., Grimson, W.E.L.: Unsupervised activity perception in crowded and complicated scenes using hierarchical Bayesian models. IEEE Trans. Pattern Anal. Mach. Intell. 31(3), 539–555 (2009) CrossRefGoogle Scholar
  71. 71.
    Wang, X., Tieu, K., Grimson, W.E.L.: Correspondence-free activity analysis and scene modeling in multiple camera views. IEEE Trans. Pattern Anal. Mach. Intell. 32(1), 56–71 (2010) CrossRefGoogle Scholar
  72. 72.
    Wu, B., Nevatia, R.: Detection and tracking of multiple, partially occluded humans by Bayesian combination of edgelet based part detectors. Int. J. Comput. Vis. 75(2), 247–266 (2007) CrossRefGoogle Scholar
  73. 73.
    Wu, S., Moore, B.E., Shah, M.: Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2054–2060 (2010) Google Scholar
  74. 74.
    Yang, M., Wu, Y., Hua, G.: Context-aware visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 31(7), 1195–1209 (2008) CrossRefGoogle Scholar
  75. 75.
    Yang, W., Wang, Y., Mori, G.: Efficient human action detection using a transferable distance function. In: Asian Conference on Computer Vision (2009) Google Scholar
  76. 76.
    Yuan, J., Liu, Z., Wu, Y.: Discriminative subvolume search for efficient action detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2442–2449 (2009) CrossRefGoogle Scholar
  77. 77.
    Zelniker, E.E., Gong, S., Xiang, T.: Global Abnormal Behaviour Detection Using a Network of CCTV Cameras. In: IEEE International Workshop on Visual Surveillance (2008) Google Scholar
  78. 78.
    Zheng, W., Gong, S., Xiang, T.: Associating groups of people. In: British Machine Vision Conference (2009) Google Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.Queen Mary University of LondonLondonUK

Personalised recommendations