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Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization

  • Yusuf OsmanlıoğluEmail author
  • Bahareh Shakibajahromi
  • Ali Shokoufandeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10746)

Abstract

Multi-camera object tracking is an efficient approach commonly used in security and surveillance systems. In a conventional multi-camera setup, a central computational unit processes large amounts of data in real time that is provided by distributed cameras. High network traffic, cost of storage on the central unit, scalability of the system, and vulnerability of the central unit to attacks are among the disadvantages of such systems. In this paper, we present an autonomous multi-camera tracking system to overcome these challenges. We assume cameras that are capable of limited computation for locally tracking a subset of objects in the scene, as well as peer-to-peer network connectivity among the cameras with a decent bandwidth that is sufficient for message passing to achieve coordination. We propose an efficient distributed algorithm for coordination and load-balancing among the cameras. We also provide experimental results to validate the utility of the proposed algorithm in comparison to a centralized algorithm.

Keywords

Multi-camera tracking Distributed assignment problem Metric labeling Primal-dual approximation 

References

  1. 1.
    Alahi, A., Ramanathan, V., Fei-Fei, L.: Tracking millions of humans in crowded spaces. In: Group and Crowd Behavior for Computer Vision, pp. 115–135 (2017)Google Scholar
  2. 2.
    Almohamad, H.A., Duffuaa, S.O.: A linear programming approach for the weighted graph matching problem. IEEE Trans. Pattern Anal. Mach. Intell. 15(5), 522–525 (1993)CrossRefGoogle Scholar
  3. 3.
    Babai, L.: Graph isomorphism in quasipolynomial time. In: Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing, pp. 684–697. ACM, June 2016Google Scholar
  4. 4.
    Baqué, P., Fleuret, F., Fua, P.: Deep occlusion reasoning for multi-camera multi-target detection. arXiv preprint arXiv:1704.05775 (2017)
  5. 5.
    Berclaz, J., Fleuret, F., Turetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1806–1819 (2011)CrossRefGoogle Scholar
  6. 6.
    Berretti, S., Del Bimbo, A., Pala, P.: A graph edit distance based on node merging. In: Enser, P., Kompatsiaris, Y., O’Connor, N.E., Smeaton, A.F., Smeulders, A.W.M. (eds.) CIVR 2004. LNCS, vol. 3115, pp. 464–472. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-27814-6_55 CrossRefGoogle Scholar
  7. 7.
    Bo Bo, N., et al.: Robust multi-camera people tracking using maximum likelihood estimation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds.) ACIVS 2013. LNCS, vol. 8192, pp. 584–595. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-02895-8_53 CrossRefGoogle Scholar
  8. 8.
    Bunke, H.: Error correcting graph matching: on the influence of the underlying cost function. IEEE Trans. Pattern Anal. Mach. Intell. 21(9), 917–922 (1999)CrossRefGoogle Scholar
  9. 9.
    Chavdarova, T., Fleuret, F.: Deep multi-camera people detection. arXiv preprint arXiv:1702.04593 (2017)
  10. 10.
    Chekuri, C., Khanna, S., Naor, J., Zosin, L.: A linear programming formulation and approximation algorithms for the metric labeling problem. SIAM J. Discrete Math. 18(3), 608–625 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the Third Annual ACM Symposium on Theory of Computing, pp. 151–158. ACM (1971)Google Scholar
  12. 12.
    Dahlhaus, E., Johnson, D.S., Papadimitriou, C.H., Seymour, P.D., Yannakakis, M.: The complexity of multiway cuts (extended abstract). In: Proceedings of the \(24^{th}\) Annual ACM Symposium on Theory of Computing, STOC 1992, pp. 241–251. ACM, New York (1992)Google Scholar
  13. 13.
    Eshel, R., Moses, Y.: Tracking in a dense crowd using multiple cameras. Int. J. Comput. Vis. 88(1), 129–143 (2010)CrossRefGoogle Scholar
  14. 14.
    Goemans, M.X., Williamson, D.P.: The primal-dual method for approximation algorithms and its application to network design problems. In: Hochbaum, D.S. (ed.) Approximation Algorithms for NP-hard Problems, pp. 144–191. PWS Publishing Co., Boston (1997)Google Scholar
  15. 15.
    Hameete, P., Leysen, S., Van Der Laan, T., Lefter, I., Rothkrantz, L.: Intelligent multi-camera video surveillance. Int. J. Inf. Technol. Secur. 4(4), 51–62 (2012)Google Scholar
  16. 16.
    Karzanov, A.V.: Minimum 0-extensions of graph metrics. Eur. J. Comb. 19(1), 71–101 (1998)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Khan, S.M., Shah, M.: A multiview approach to tracking people in crowded scenes using a planar homography constraint. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 133–146. Springer, Heidelberg (2006).  https://doi.org/10.1007/11744085_11 CrossRefGoogle Scholar
  18. 18.
    Khan, S.M., Yan, P., Shah, M.: A homographic framework for the fusion of multi-view silhouettes. In: IEEE 11th International Conference on Computer Vision 2007, ICCV 2007, pp. 1–8. IEEE (2007)Google Scholar
  19. 19.
    Kleinberg, J., Tardos, É.: Approximation algorithms for classification problems with pairwise relationships: metric labeling and markov random fields. J. ACM 49(5), 616–639 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Livi, L., Rizzi, A.: The graph matching problem. Pattern Anal. Appl. 16(3), 253–283 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Osmanlıoğlu, Y., Ontañón, S., Hershberg, U., Shokoufandeh, A.: Efficient approximation of labeling problems with applications to immune repertoire analysis. In 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2410–2415. IEEE (2016)Google Scholar
  22. 22.
    Pentico, D.W.: Assignment problems: a golden anniversary survey. Eur. J. Oper. Res. 176(2), 774–793 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Stillman, S., Tanawongsuwan, R., Essa, I.: Tracking multiple people with multiple cameras. In: International Conference on Audio-and Video-based Biometric Person Authentication (1999)Google Scholar
  24. 24.
    Williams, M.L., Wilson, R.C., Hancock, E.R.: Deterministic search for relational graph matching. Pattern Recogn. 32(7), 1255–1271 (1999)CrossRefGoogle Scholar
  25. 25.
    Wu, Z., Hristov, N.I., Hedrick, T.L., Kunz, T.H., Betke, M.: Tracking a large number of objects from multiple views. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1546–1553. IEEE (2009)Google Scholar
  26. 26.
    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 2008, CVPR 2008, pp. 1–8. IEEE (2008)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Section of Biomedical Image AnalysisUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Department of Computer ScienceDrexel UniversityPhiladelphiaUSA

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