Advertisement

Learning the Three Factors of a Non-overlapping Multi-camera Network Topology

  • Xiaotang Chen
  • Kaiqi Huang
  • Tieniu Tan
Part of the Communications in Computer and Information Science book series (CCIS, volume 321)

Abstract

In this paper, we propose an unsupervised approach for learning the three factors of the topology of a non-overlapping multi-camera network, which are nodes, links, and transition time distributions. It is a cross-correlation based method. Different from previous methods, the proposed method can deal with large amounts of data without considering the size of time window. The connectivity between nodes is estimated based on the N-neighbor accumulated cross-correlations, as well as the transition time distribution for each link. Furthermore, integrated with similarity cues, the proposed method can be extended into weighted cross-correlation models for better performance. Experimental results both on simulated and real-life datasets demonstrate the effectiveness of the proposed method.

Keywords

Topology recovering Transition time distribution Camera network Non-overlapping views 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ellis, T.J., Makris, D., Black, J.K.: Learning a Multi-camera Topology. In: Joint IEEE Workshop on Visual Surveillance and Performance Evaluation of Tracking and Surveillance (VS-PETS), pp. 165–171 (2003)Google Scholar
  2. 2.
    Cai, Y., Huang, K., Tan, T., Pietikäinen, M.: Recovering the Topology of Multiple Cameras by Finding Continuous Paths in a Trellis. In: ICPR, pp. 3541–3544 (2010)Google Scholar
  3. 3.
    Niu, C., Grimson, E.: Recovering Non-overlapping Network Topology using Far-field Vehicle Tracking Data. In: ICPR, pp. 944–949 (2006)Google Scholar
  4. 4.
    Marinakis, D., Giguère, P., Dudek, G.: Learning network topology from simple sensor data. In: Kobti, Z., Wu, D. (eds.) Canadian AI 2007. LNCS (LNAI), vol. 4509, pp. 417–428. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across Multiple Cameras with Disjoint Views. In: ICCV, pp. 952–957 (2003)Google Scholar
  6. 6.
    Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of Non-overlapping Camera Network Topology by Measuring Statistical Dependence. In: ICCV, pp. 1842–1849 (2005)Google Scholar
  7. 7.
    Nam, Y., Ryu, J., Choi, Y., Cho, W.: Learning Spatio-temporal Topology of a Multi-camera Network by Tracking Multiple People. Proceedings of World Academy of Science, Engineering and Technology 24, 175–180 (2007)Google Scholar
  8. 8.
    Makris, D., Ellis, T., Black, J.: Bridging the Gaps between Cameras. In: CVPR, vol. 2, pp. 205–210 (2004)Google Scholar
  9. 9.
    Zou, X., Bhanu, B., Roy-Chowdhury, A.: Continuous Learning of a Multilayered Network Topology in a Video Camera Network. EURASIP Journal on Image and Video Processing (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Xiaotang Chen
    • 1
  • Kaiqi Huang
    • 1
  • Tieniu Tan
    • 1
  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesChina

Personalised recommendations