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Large-Scale Camera Topology Mapping: Application to Re-identification

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Person Re-Identification

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

In this chapter we describe the problem of camera network topology mapping, which is a critical precursor to person re-identification in large camera networks. After surveying previous approaches to this problem we describe “exclusion”, a practical, robust method for deriving a topology estimate that scales to thousands of cameras. We then consider re-identification within such networks by modelling and matching target appearance. By combining a simple appearance model with the topology estimate generated by exclusion, person re-identification can be accomplished within far larger scale networks than would be possible using appearance matching alone.

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References

  1. Anjum, N., Cavallaro, A.: Automated localization of a camera network. IEEE Intell. Syst. 27(5) (2012)

    Google Scholar 

  2. Bishop, C.: Pattern Recognition and Machine Learning. Springer, New York (2006)

    Google Scholar 

  3. Brand, M., Antone, M., Teller, S.: Spectral solution of large-scale extrinsic camera calibration as a graph embedding problem. In: Proceedings of 8th European Conference on Computer Vision, pp. 262–273 (2004)

    Google Scholar 

  4. Buehler, C.: Computerized method and apparatus for determining field-of-view relationships among multiple image sensors. United States Patent 7286157 (2007)

    Google Scholar 

  5. Chen, C.H., Yao, Y., Page, D., Abidi, B., Koschan, A., Abidi, M.: Camera handoff and placement for automated tracking systems with multiple omnidirectional cameras. Comput. Vis. Image Underst. 114, 179–197 (2010)

    Article  Google Scholar 

  6. Chen, K.W., Lai, C.C., Lee, P.J., Chen, C.S., Hung, Y.P.: Adaptive learning for target tracking and true linking discovering across multiple non-overlapping cameras. IEEE Trans. Multimed. 13(4), 625–638 (2011)

    Article  Google Scholar 

  7. Chen, K.Y., Huang, C.L., Hsu, S.C., Chang, I.C.: Multiple objects tracking across multiple non-overlapped views. In: Ho, Y.S. (ed.) Advances in Image and Video Technology. Lecture Notes in Computer Science, vol. 7088, pp. 128–140 (2012)

    Google Scholar 

  8. Chen, X., Huang, K., Tan, T.: Learning the three factors of a non-overlapping multi-camera network topology. In: Pattern Recognition, Communications in Computer and Information Science, vol. 321, pp. 104–112 (2012)

    Google Scholar 

  9. Detmold, H., van den Hengel, A., Dick, A., Cichowski, A., Hill, R., Kocadag, E., Yarom, Y., Falkner, K., Munro, D.: Estimating camera overlap in large and growing networks. In: Proceedings of ACM/IEEE International Conference on Distributed Smart Cameras (2008)

    Google Scholar 

  10. Dick, A., Brooks, M.J.: A stochastic approach to tracking objects across multiple cameras. In: Proceedings of Australian Joint Conference on, Artificial Intelligence (AI’04), pp. 160–170 (2004)

    Google Scholar 

  11. Ellis, T.J., Makris, D., Black, J.: 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 

  12. Gilbert, A., Bowden, R.: Tracking objects across cameras by incrementally learning inter-camera colour calibraion and patterns of activity. In: European Conference on Computer Vision, vol. 2, pp. 125–136 (2006)

    Google Scholar 

  13. van den Hengel, A., Dick, A., Detmold, H., Cichowski, A., Hill, R.: Finding camera overlap in large surveillance networks. In: Yagi, Y., Kang, S., Kweon, I., Zha, H. (eds.) Asian Conference on Computer Vision. Lecture Notes in Computer Science, vol. 4843, pp. 375–384 (2007)

    Google Scholar 

  14. Javed, O., Rasheed, Z., Shafique, K., Shah, M.: Tracking across multiple cameras with disjoint views. In: Proceedings of IEEE International Conference on Computer Vision, pp. 952–957 (2003)

    Google Scholar 

  15. Javed, O., Shafique, K., Rasheed, Z., Shah, M.: Modeling inter-camera spaceâĂŞtime and appearance relationships for tracking across non-overlapping views. Comput. Vis. Image Underst. 109(2), 146–162 (2008)

    Article  Google Scholar 

  16. Kang, J., Cohen, I., Medioni, G.: Persistent objects tracking across multiple non overlapping cameras. In: IEEE Workshop on Motion and Video, Computing, vol. 2, pp. 112–119 (2005)

    Google Scholar 

  17. Khan, S., Javed, O., Rasheed, Z., Shah, M.: Human tracking in multiple cameras. In: Proceedings of IEEE International Conference on Computer Vision, pp. I:331–336 (2001)

    Google Scholar 

  18. Lee, L., Romano, R., Stein, G.: Monitoring activities from multiple video streams: establishing a common coordinate frame. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 758–767 (2000)

    Article  Google Scholar 

  19. Lim, F., Leoputra, W., Tan, T.: Non-overlapping distributed tracking system utilizing particle filter. J. VLSI Signal Process. 49, 343–362 (2007)

    Article  Google Scholar 

  20. Loy, C.C., Xiang, T., Gong, S.: Multi-camera activity correlation analysis. In: IEEE Conference on Computer Vision and, Pattern Recognition, pp. 1988–1995 (2009)

    Google Scholar 

  21. Loy, C.C., Xiang, T., Gong, S.: Incremental activity modelling in multiple disjoint cameras. IEEE Trans. Pattern Anal. Mach. Intell. 34(9), 1799–1813 (2012)

    Article  Google Scholar 

  22. Marinakis, D., Dudek, G.: Self-calibration of a vision-based sensor network. Image Vis. Comput. 27, 116–130 (2009)

    Google Scholar 

  23. Mavrinac, A., Chen, X.: Modeling coverage in camera networks: a survey. Int. J. Comput. Vis. 101(1), 205–226 (2013)

    Article  MathSciNet  Google Scholar 

  24. Raghavan, V., Bollmann, P., Jung, G.S.: A critical investigation of recall and precision as measures of retrieval system performance. ACM Trans. Inform. Syst. 7(3), 205–229 (1989)

    Article  Google Scholar 

  25. Rahimi, A., Dunagan, B., Darrell, T.: Simultaneous calibration and tracking with a network of non-overlapping sensors. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), pp. 187–194 (2004)

    Google Scholar 

  26. Rahimi, A., Dunagan, B., Darrell, T.: Tracking people with a sparse network of bearing sensors. In: European Conference on Computer Vision, pp. 507–518 (2004)

    Google Scholar 

  27. Soto, C., Song, B., Roy-Chowdhury, A.: Distributed multi-target tracking in a self-configuring camera network. In: IEEE Conference on Computer Vision and, Pattern Recognition, pp. 1486–1493 (2009)

    Google Scholar 

  28. Stauffer, C.: Learning to track objects through unobserved regions. In: IEEE Computer Society Workshop on Motion and Video Computing, pp. II: 96–102 (2005)

    Google Scholar 

  29. Stauffer, C., Grimson, W.E.L.: Learning patterns of activity using real-time tracking. IEEE Trans. Pattern Anal. Mach. Intell. 22(8), 747–757 (2000)

    Article  Google Scholar 

  30. Tieu, K., Dalley, G., Grimson, W.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: Proceedings of IEEE International Conference on Computer Vision, pp. II: 1842–1849 (2005)

    Google Scholar 

  31. Wang, X.: Intelligent multi-camera video surveillance: a review. Pattern Recogn. Lett. 34(1), 3–19 (2013)

    Article  Google Scholar 

  32. Yao, R., Shi, Q., Shen, C., Zhang, Y., van den Hengel, A.: Robust tracking with weighted online structured learning. In: European Conference on Computer Vision (2012)

    Google Scholar 

  33. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and, Pattern Recognition, pp. 1–8 (2008)

    Google Scholar 

  34. Zhao, T., Aggarwal, M., Kumar, R., Sawhney, H.: Real-time wide area multi-camera stereo tracking. In: IEEE Conference Computer Vision and, Pattern Recognition, vol. 1, pp. 976–983 (2005)

    Google Scholar 

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Author information

Authors and Affiliations

  1. School of Computer Science, University of Adelaide, North Terrace, Adelaide, 5005, SA, Australia

    Anthony Dick

  2. University of Adelaide, Adelaide, SA, Australia

    Anton van den Hengel

  3. Snap Network Surveillance, Adelaide, SA, Australia

    Henry Detmold

Authors
  1. Anthony Dick
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  2. Anton van den Hengel
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  3. Henry Detmold
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Corresponding author

Correspondence to Anthony Dick .

Editor information

Editors and Affiliations

  1. Queen Mary University, London, United Kingdom

    Shaogang Gong

  2. University of Verona, Italy

    Marco Cristani

  3. National University of Singapore, Singapore

    Shuicheng Yan

  4. The Chinese University of Hong Kong, Shatin, Hong Kong SAR

    Chen Change Loy

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© 2014 Springer-Verlag London

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Dick, A., Hengel, A.v.d., Detmold, H. (2014). Large-Scale Camera Topology Mapping: Application to Re-identification. In: Gong, S., Cristani, M., Yan, S., Loy, C. (eds) Person Re-Identification. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-6296-4_19

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  • DOI: https://doi.org/10.1007/978-1-4471-6296-4_19

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6295-7

  • Online ISBN: 978-1-4471-6296-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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