Multi-camera Behaviour Correlation

  • Shaogang Gong
  • Tao Xiang


For gaining a more holistic sense of situational awareness, a primary goal for a multi-camera system is to provide a more complete record and survey a trail of object activities in wide-area spaces, both individually and collectively. This allows for a global interpretation of objects’ latent behaviour patterns and intent. In a multicamera system, disjoint cameras with non-overlapping field of views are more prevalent, due to the desire to maximise spatial coverage in a wide-area scene. However, global behaviour analysis across multiple disjoint cameras is hampered by a number of obstacles such as inter-camera visual appearance variation, unknown and arbitrary inter-camera gaps, lack of visual details and crowdedness, and visual context variation. To overcome these obstacles, a key to visual analysis of multi-camera behaviour lies on how well a model can correlate partial observations of object behaviours from different locations in order to carry out ‘joined-up reasoning’. In this chapter, we describe a framework for modelling a joined-up representation of a synchronised global space, within which local activities from different observational viewpoints can be analysed and interpreted holistically. The focus is on developing a suitable mechanism capable of discovering and quantifying unknown correlations in temporal ordering and temporal delays among different camera views.


Canonical Correlation Canonical Correlation Analysis Camera View Camera Network Observational Viewpoint 
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. Cohen, N., Gatusso, J., MacLennan-Brown, K.: CCTV Operational Requirements Manual—Is Your CCTV System Fit for Purpose? Home Office Scientific Development Branch, version 4 (55/06) edition (2006) Google Scholar
  2. Fraser, A.M., Swinney, H.L.: Independent coordinates for strange attractors from mutual information. Phys. Rev. 33(2), 1134–1140 (1986) MathSciNetMATHGoogle Scholar
  3. Hardoon, D., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis; an overview with application to learning methods. Neural Comput. 16(12), 2639–2664 (2004) MATHCrossRefGoogle Scholar
  4. Kruegle, H.: CCTV Surveillance: Video Practices and Technology. Butterworth/Heinemann, Stoneham/London (2006) Google Scholar
  5. Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, June 2009, pp. 1988–1995 (2009a) Google Scholar
  6. 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, Kyoto, Japan, October 2009, pp. 120–127 (2009b) Google Scholar
  7. Russell, D., Gong, S.: Minimum Cuts of a Time-varying Background. In: British Machine Vision Conference, Edinburgh, UK, September 2006, pp. 809–818 (2006) Google Scholar
  8. Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–758 (2000) CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  1. 1.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK
  2. 2.School of Electronic Engineering and Computer ScienceQueen Mary University of LondonLondonUK

Personalised recommendations