Multimedia Tools and Applications

, Volume 67, Issue 1, pp 289–309 | Cite as

Inference topology of distributed camera networks with multiple cameras

Article

Abstract

This paper proposes an inference method to construct the topology of a camera network with overlapping and non-overlapping fields of view for a commercial surveillance system equipped with multiple cameras. It provides autonomous object detection, tracking and recognition in indoor or outdoor urban environments. The camera network topology is estimated from object tracking results among and within FOVs. The merge-split method is used for object occlusion in a single camera and an EM-based approach for extracting the accurate object feature to track moving people and establishing object correspondence across multiple cameras. The appearance of moving people and the transition time between entry and exit zones is measured to track moving people across blind regions of multiple cameras with non-overlapping FOVs. Our proposed method graphically represents the camera network topology, as an undirected weighted graph using the transition probabilities and 8-directional chain code. The training phase and the test were run with eight cameras to evaluate the performance of our method. The temporal probability distribution and the undirected weighted graph are shown in the experiments.

Keywords

Video surveillance Multi-camera Intelligent system People tracking Camera network topology 

References

  1. 1.
    Akyildiz IF, Melodia T, Chowdhury KR (2007) A survey on wireless multimedia sensor networks. Comput Netw 51:921–960CrossRefGoogle Scholar
  2. 2.
    Black J, Ellis TJ, Makris D (2004) Wide area surveillance with a multi camera network. Intelligent Distributed Surveillance Systems IDSS04, pp 21–25. doi: 10.1049/ic:20040092 Google Scholar
  3. 3.
    Cai Q, Aggarwal J (1996) Tracking human motion using multiple cameras. In: Proceedings of the 13th international conference on pattern recognition, vol 3, pp 68–72Google Scholar
  4. 4.
    Cai Q, Aggarwal JK (1998) Automatic tracking of human motion in indoor scenes across multiple synchronized video streams. In: Proceedings of the sixth international conference on computer vision, ICCV ’98. IEEE Computer Society, Washington, DC, USA, pp 356–362Google Scholar
  5. 5.
    Chen M, González S, Cao H, Zhang Y, Vuong S (2010) Enabling low bit-rate and reliable video surveillance over practical wireless sensor network. J Supercomput pp 1–14. doi: 10.1007/s11227-010-0475-2
  6. 6.
    Chen M, Gonzalez S, Leung V, Zhang Q, Li M (2010) A 2g-rfid-based e-healthcare system. Wirel Commun 17:37–43. doi:10.1109/MWC.2010.5416348 CrossRefGoogle Scholar
  7. 7.
    Chen M, Gonzalez S, Leung VCM (2007) Applications and design issues for mobile agents in wireless sensor networks. Wirel Commun IEEE 14(6):20–26. doi:10.1109/MWC.2007.4407223 CrossRefGoogle Scholar
  8. 8.
    Chen M, Gonzalez S, Vasilakos A, Cao H, Leung VC (2011) Body area networks: a survey. Mob Netw Appl 16:171–193. doi:10.1007/s11036-010-0260-8 CrossRefGoogle Scholar
  9. 9.
    Chen M, Gonzalez S, Zhang Q, Leung VC (2010) Code-centric rfid system based on software agent intelligence. IEEE Intell Syst 25:12–19CrossRefGoogle Scholar
  10. 10.
    Chen M, Kwon T, Yuan Y, Choi Y, Leung VCM (2007) Mobile agent-based directed diffusion in wireless sensor networks. EURASIP J Appl Signal Process 2007:13. doi:10.1155/2007/36871 Google Scholar
  11. 11.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J R Stat Soc B (Methodological) 39(1):1–38MathSciNetMATHGoogle Scholar
  12. 12.
    Dick AR, Brooks MJ (2004) A stochastic approach to tracking objects across multiple cameras. In: Australian conference on artificial intelligence, pp 160–170Google Scholar
  13. 13.
    Ellis TJ, Makris D, Black JK (2003) Learning a multi-camera topology. In: IEEE international workshop on visual surveillance and performance evaluation of tracking and surveillance, pp 165–171Google Scholar
  14. 14.
    Fisher RB (2002) Self-organization of randomly placed sensors. In: Proceedings of the 7th European conference on computer vision-part IV, ECCV ’02. Springer, London, UK, pp 146–160. http://dl.acm.org/citation.cfm?id=645318.649357
  15. 15.
    Funiak S, Guestrin C, Paskin M, Sukthankar R (2006) Distributed localization of networked cameras. In: IPSN ’06: proceedings of the 5th international conference on information processing in sensor networks. ACM, New York, NY, USA, pp 34–42. doi:10.1145/1127777.1127786 CrossRefGoogle Scholar
  16. 16.
    Gilbert A, Bowden R (2008) Incremental, scalable tracking of objects inter camera. Comput Vis Image Underst 111(1):43–58CrossRefGoogle Scholar
  17. 17.
    Haritaoglu I, Harwood D, Davis L (1998) W4: who? when? where? what? a real time system for detecting and tracking people. In: Proceedings of third IEEE international conference on automatic face and gesture recognition, pp 222–227Google Scholar
  18. 18.
    Haritaoglu I, Harwood D, Davis LS (1998) W4s: a real-time system detecting and tracking people in 2 1/2d. In: European conference on computer vision, pp 877–892Google Scholar
  19. 19.
    Huang T, Russell S (1997) Object identification in a bayesian context. In: Proceedings of the fifteenth international joint conference on artificial intelligence. Morgan Kaufmann, pp 1276–1283Google Scholar
  20. 20.
    Intel: Open computer vision library. http://sourceforge.net/projects/opencvlibrary/. Accessed 30 Jan 2012
  21. 21.
    Javed O, Rasheed Z, Shafique K, Shah M (2003) Tracking across multiple cameras with disjoint views. In: Computer vision, 2003. Proceedings. Ninth IEEE international conference, vol 2, pp 952–957Google Scholar
  22. 22.
    KaewTrakulPong P, Bowden R (2003) A real time adaptive visual surveillance system for tracking low-resolution colour targets in dynamically changing scenes. Image Vis Comput 21(10):913–929CrossRefGoogle Scholar
  23. 23.
    Kelly PH, Katkere A, Kuramura DY, Moezzi S, Chatterjee S (1995) An architecture for multiple perspective interactive video. In: Proceedings of the third ACM international conference on Multimedia, MULTIMEDIA ’95. ACM, New York, NY, USA, pp 201–212CrossRefGoogle Scholar
  24. 24.
    Kettnaker V, Zabih R (1999) Counting people from multiple cameras. In: Proceedings of the IEEE international conference on multimedia computing and systems, vol 2, pp 267–271Google Scholar
  25. 25.
    Kim M, Nam Y, Kim S, Cho W (2009) A hardhat detection system for preventing work zone accidents in complex scene images. In: Arabnia HR, Schaefer G (eds) IPCV, CSREA Press, pp 492–500Google Scholar
  26. 26.
    Makris D, Ellis T (2005) Learning semantic scene models from observing activity in visual surveillance. IEEE Trans Syst Man Cybern, Part B, Cybern 35(3):397–408. doi:10.1109/TSMCB.2005.846652 CrossRefGoogle Scholar
  27. 27.
    Makris D, Ellis T, Black J (2004) Bridging the gaps between cameras. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVRP 2004, vol 2, pp 205–210. 10.1109/CVPR.2004.1315165
  28. 28.
    Marinakis D, Dudek G, Carlo M, Maximization E (2005) Learning sensor network topology through monte carlo expectation maximization. In: IEEE int’l conf on robotics and automation, pp 4581–4587Google Scholar
  29. 29.
    Niu C, Grimson E (2006) Recovering non-overlapping network topology using far-field vehicle tracking data. In: Proceedings of the 18th international conference on pattern recognition, vol 04, ICPR ’06. IEEE Computer Society, Washington, DC, USA, pp 944–949. doi: 10.1109/ICPR.2006.985 Google Scholar
  30. 30.
    Rahimi A, Dunagan B, Darrell T (2004) Simultaneous calibration and tracking with a network of non-overlapping sensors. In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004. CVPR 2004, vol 1, pp 187–194. doi: 10.1109/CVPR.2004.1315031
  31. 31.
    Redner RA, Walker HF (1984) Mixture densities, maximum likelihood and the Em algorithm. SIAM Rev 26(2):195–239MathSciNetMATHCrossRefGoogle Scholar
  32. 32.
    Stauffer C (2005) Learning to track objects through unobserved regions. In: Proceedings of the IEEE workshop on motion and video computing (WACV/MOTION’05) - vol 2 - vol 02, WACV-MOTION ’05, IEEE Computer Society, Washington, DC, USA, pp 96–102Google Scholar
  33. 33.
    Sturges J, Whitfield TWA (1995) Locating basic colours in the munsell space. Color Res Appl 20(6):364–376CrossRefGoogle Scholar
  34. 34.
    Tieu K, Dalley G, Grimson W (2005) Inference of non-overlapping camera network topology by measuring statistical dependence. In: Tenth IEEE international conference on computer vision, 2005. ICCV 2005, vol 2, pp 1842–1849Google Scholar
  35. 35.
    Welch G, Bishop G (1995) An introduction to the Kalman filter. Tech rep, University of North Carolina at Chapel Hill, Chapel Hill, NC, USAGoogle Scholar
  36. 36.
    Zou X, Bhanu B, Song B, Chowdhury AKR (2007) Determining topology in a distributed camera network. In: IEEE international conference on image processing, 2007. ICIP 2007, vol 5, pp 133–136. doi: 10.1109/ICIP.2007.4379783

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Mobile Systems Design Laboratory, Department of Electrical and Computer EngineeringStony Brook University-SUNYStony BrookUSA
  2. 2.Division of Information & CommunicationBaekseok UniversityCheonanKorea
  3. 3.Seoul National University of Science and TechnologySeoulSouth Korea

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