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2018 | OriginalPaper | Buchkapitel

Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization

verfasst von : Yusuf Osmanlıoğlu, Bahareh Shakibajahromi, Ali Shokoufandeh

Erschienen in: Energy Minimization Methods in Computer Vision and Pattern Recognition

Verlag: Springer International Publishing

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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.

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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)

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)CrossRef

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 2016

Baqué, P., Fleuret, F., Fua, P.: Deep occlusion reasoning for multi-camera multi-target detection. arXiv preprint arXiv:1704.05775 (2017)

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)CrossRef

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 CrossRef

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 CrossRef

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)CrossRef

Chavdarova, T., Fleuret, F.: Deep multi-camera people detection. arXiv preprint arXiv:1702.04593 (2017)

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)MathSciNetCrossRefMATH

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)

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)

13.

Eshel, R., Moses, Y.: Tracking in a dense crowd using multiple cameras. Int. J. Comput. Vis. 88(1), 129–143 (2010)CrossRef

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)

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)

16.

Karzanov, A.V.: Minimum 0-extensions of graph metrics. Eur. J. Comb. 19(1), 71–101 (1998)MathSciNetCrossRefMATH

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 CrossRef

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)

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)MathSciNetCrossRefMATH

20.

Livi, L., Rizzi, A.: The graph matching problem. Pattern Anal. Appl. 16(3), 253–283 (2013)MathSciNetCrossRefMATH

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)

22.

Pentico, D.W.: Assignment problems: a golden anniversary survey. Eur. J. Oper. Res. 176(2), 774–793 (2007)MathSciNetCrossRefMATH

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)

24.

Williams, M.L., Wilson, R.C., Hancock, E.R.: Deterministic search for relational graph matching. Pattern Recogn. 32(7), 1255–1271 (1999)CrossRef

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)

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)

Titel: Autonomous Multi-camera Tracking Using Distributed Quadratic Optimization
verfasst von: Yusuf Osmanlıoğlu
Bahareh Shakibajahromi
Ali Shokoufandeh
Verlag: Springer International Publishing
Buch: Energy Minimization Methods in Computer Vision and Pattern Recognition
Print ISBN: 978-3-319-78198-3

Electronic ISBN: 978-3-319-78199-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-78199-0_12

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