Behaviour-Based Object Classifier for Surveillance Videos

  • Virginia Fernandez Arguedas
  • Krishna Chandramouli
  • Ebroul Izquierdo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 255)


In this paper, a study on effective exploitation of geometrical features for classifying surveillance objects into a set of pre-defined semantic categories is presented. The geometrical features correspond to object’s motion, spatial location and velocity. The extraction of these features is based on object’s trajectory corresponding to object’s temporal evolution. These geometrical features are used to build a behaviour-based classifier to assign semantic categories to the individual blobs extracted from surveillance videos. The proposed classification framework has been evaluated against conventional object classifiers based on visual features extracted from semantic categories defined on AVSS 2007 surveillance dataset.


Object classification geometrical models surveillance videos object tracking motion features 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    McCahill, M., Norris, C.: Estimating the extent, sophistication and legality of CCTV in London. In: CCTV, pp. 51–66 (2003)Google Scholar
  2. 2.
    Piciarelli, C., Micheloni, C., Foresti, G.: Trajectory-based anomalous event detection. IEEE Transactions on Circuits and Systems for Video Technology 18(11), 1544–1554 (2008)CrossRefGoogle Scholar
  3. 3.
    Schiele, B., Crowley, J.: Recognition without correspondence using multidimensional receptive field histograms. International Journal of Computer Vision 36(1), 31–50 (2000)CrossRefGoogle Scholar
  4. 4.
    Pontil, M., Verri, A.: Support vector machines for 3D object recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(6), 637–646 (1998)CrossRefGoogle Scholar
  5. 5.
    Bashir, F., Khokhar, A., Schonfeld, D.: Real-time motion trajectory-based indexing and retrieval of video sequences. IEEE Transactions on Multimedia 9(1), 58–65 (2007)CrossRefGoogle Scholar
  6. 6.
    Javed, O., Shah, M.: Tracking and Object Classification for Automated Surveillance. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2353, pp. 343–357. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Stauffer, C., Grimson, W.: Learning patterns of activity using real-time tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(8), 747–757 (2000)CrossRefGoogle Scholar
  8. 8.
    Fernandez Arguedas, V., Zhang, Q., Chandramouli, K., Izquierdo, E.: Multi-feature fusion for surveillance video indexing. In: 12th International Workshop on Image Analysis for Multimedia Interactive Services (April 2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Virginia Fernandez Arguedas
    • 1
  • Krishna Chandramouli
    • 1
  • Ebroul Izquierdo
    • 1
  1. 1.Multimedia and Vision Research Group, School of Electronic Engineering and Computer ScienceQueen Mary, University of LondonLondonUK

Personalised recommendations