Issue
RAIRO-Oper. Res.
Volume 55, Number 4, July-August 2021
Decision and Optimization in Service, Control and Engineering (CoDIT2019-DOSCE)
Page(s) 2069 - 2091
DOI https://doi.org/10.1051/ro/2021092
Published online 08 July 2021
  • T. Andersen and S. Tirthapura, Wireless sensor deployment for 3D coverage with constraints. In: 2009 Sixth International Conference on Networked Sensing Systems (INSS). IEEE (2009). [Google Scholar]
  • F. Angella, L. Reithler and F. Gallesio, Optimal deployment of cameras for video surveillance systems. In: 2007 IEEE Conference on Advanced Video and Signal Based Surveillance. IEEE (2007). [Google Scholar]
  • J. Beasley, An algorithm for set covering problem. Eur. J. Oper. Res. 31 (1987) 85–93. [CrossRef] [Google Scholar]
  • J.E. Beasley, OR-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41 (1990) 1069–1072. [CrossRef] [Google Scholar]
  • J.E. Beasley, A lagrangean heuristic for set covering problems. In: Combinatorial Optimization. Springer, Berlin Heidelberg (1992) 325–326. [Google Scholar]
  • J.E. Beasley and K. Jørnsten, Enhancing an algorithm for set covering problems. Eur. J. Oper. Res. 58 (1992) 293–300. [CrossRef] [Google Scholar]
  • R. Bodor, P. Schrater and N. Papanikolopoulos, Multi-camera positioning to optimize task observability. In: Proceedings IEEE Conference on Advanced Video and Signal Based Surveillance, 2005. IEEE (2005). [Google Scholar]
  • M. Brévilliers, J. Lepagnot, J. Kritter and L. Idoumghar, Parallel preprocessing for the optimal camera placement problem. Int. J. Model. Optim. 8 (2018) 33–40. [Google Scholar]
  • M. Brévilliers, J. Lepagnot, L. Idoumghar, M. Rebai and J. Kritter, Hybrid differential evolution algorithms for the optimal camera placement problem. J. Syst. Inf. Technol. 20 (2018) 446–467. [Google Scholar]
  • V. Chvátal, A greedy heuristic for the set-covering problem. Math. Oper. Res. 4 (1979) 233–235. [CrossRef] [MathSciNet] [Google Scholar]
  • N. Conci and L. Lizzi, Camera placement using particle swarm optimization in visual surveillance applications. In: 2009 16th IEEE International Conference on Image Processing (ICIP). IEEE (2009). [Google Scholar]
  • C.K. Cowan and P.D. Kovesi, Automatic sensor placement from vision task requirements. IEEE Trans. Pattern Anal. Mach. Intell. 10 (1988) 407–416. [Google Scholar]
  • E.W. Dijkstra, A note on two problems in connexion with graphs. Numer. Math. 1 (1959) 269–271. [CrossRef] [MathSciNet] [Google Scholar]
  • U.M. Erdem and S. Sclaroff, Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements. Comput. Vision Image Understand. 103 (2006) 156–169. [Google Scholar]
  • E.P.C. Fantini and L. Chaimowicz, Coverage in Arbitrary 3D Environments: The Art Gallery Problem in Shooter Games. IEEE (2013). [Google Scholar]
  • U. Feige, A threshold of ln n for approximating set cover. J. ACM 45 (1998) 634–652. [Google Scholar]
  • T.A. Feo and M.G. Resende, A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8 (1989) 67–71. [CrossRef] [MathSciNet] [Google Scholar]
  • C. Gao, X. Yao, T. Weise and J. Li, An efficient local search heuristic with row weighting for the unicost set covering problem. Eur. J. Oper. Res. 246 (2015) 750–761. [Google Scholar]
  • P. Hart, N. Nilsson and B. Raphael, A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4 (1968) 100–107. [Google Scholar]
  • E. Horster and R. Lienhart, Approximating optimal visual sensor placement: In 2006 IEEE International Conference on Multimedia and Expo. IEEE (2006). [Google Scholar]
  • IBM. IBM CPLEX Optimiser. https://www-01.ibm.com/software/commerce/optimization/cplex-optimizer. Accessed: 2018-12-03. [Google Scholar]
  • S. Indu, S. Chaudhury, N. Mittal and A. Bhattacharyya, Optimal sensor placement for surveillance of large spaces. In: 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC). IEEE (2009). [Google Scholar]
  • R.M. Karp, Reducibility among combinatorial problems. In: Complexity of Computer Computations. Springer, US (1972) 85–103. [Google Scholar]
  • K.R. Konda and N. Conci, Optimal configuration of PTZ camera networks based on visual quality assessment and coverage maximization. In: 2013 Seventh International Conference on Distributed Smart Cameras (ICDSC). IEEE (2013). [Google Scholar]
  • J. Kritter, M. Brévilliers, J. Lepagnot and L. Idoumghar, On the optimal placement of cameras for surveillance and the underlying set cover problem. Appl. Soft Comput. 74 (2019) 133–153. [Google Scholar]
  • J. Kritter, M. Brévilliers, J. Lepagnot and L. Idoumghar, On the real-world applicability of state-of-the-art algorithms for the optimal camera placement problem. In: 6th 2019 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE (2019). [Google Scholar]
  • J. Kritter, M. Brévilliers, J. Lepagnot and L. Idoumghar, On the use of human-assisted optimisation for the optimal camera placement problem and the surveillance of urban events. In: 7th 2020 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE (2020). [Google Scholar]
  • G. Lan, G.W. DePuy and G.E. Whitehouse, An effective and simple heuristic for the set covering problem. Eur. J. Oper. Res. 176 (2007) 1387–1403. [Google Scholar]
  • J. Liu, S. Sridharan and C. Fookes, Recent advances in camera planning for large area surveillance. ACM Comput. Surv. 49 (2016) 1–37. [Google Scholar]
  • E. Marchiori and A. Steenbeek, An iterated heuristic algorithm for the set covering problem. In: 2nd Workshop on Algorithm Engineering (WAE98). Saarbreucken (1998) 155–166. [Google Scholar]
  • MeshLab. http://www.meshlab.net. Accessed: 2018-12-03. [Google Scholar]
  • T. Möller and B. Trumbore, Fast, minimum storage ray-triangle intersection. J. Graphics Tools 2 (1997) 21–28. [Google Scholar]
  • Y. Morsly, M.S. Djouadi and N. Aouf, On the best interceptor placement for an optimally deployed visual sensor network. In: 2010 IEEE International Conference on Systems, Man and Cybernetics. IEEE (2010). [Google Scholar]
  • V.P. Munishwar and N.B. Abu-Ghazaleh, Coverage algorithms for visual sensor networks. ACM Trans. Sensor Netw. 9 (2013) 1–36. [Google Scholar]
  • A.T. Murray, K. Kim, J.W. Davis, R. Machiraju and R. Parent, Coverage optimization to support security monitoring. Comput. Environ. Urban Syst. 31 (2007) 133–147. [Google Scholar]
  • G. Olague and R. Mohr, Optimal camera placement for accurate reconstruction. Pattern Recogn. 35 (2002) 927–944. [Google Scholar]
  • OpenStreetMap, Elements – OpenStreetMap wiki. https://wiki.openstreetmap.org/wiki/Elements. Accessed: 2018-12-03. [Google Scholar]
  • OpenStreetMap. Map features – OpenStreetMap wiki. https://wiki.openstreetmap.org/wiki/Map_Features. Accessed: 2018-12-03 [Google Scholar]
  • J. O’Rourke, Art Gallery Theorems and Algorithms. In: Vol. 3 of International Series of Monographs on Computer Science. Oxford University Press (1987). [Google Scholar]
  • OSM2World, OSM2World. http://osm2world.org. Accessed: 2018-12-03 [Google Scholar]
  • M. Rebai, M.L. Berre, F. Hnaien and H. Snoussi, Exact biobjective optimization methods for camera coverage problem in three-dimensional areas. IEEE Sensors J. 16 (2016) 3323–3331. [Google Scholar]
  • V. Satopaa, J. Albrecht, D. Irwin and B. Raghavan, Finding a “kneedle’’ in a haystack: detecting knee points in system behavior. In: 2011 31st International Conference on Distributed Computing Systems Workshops. IEEE (2011). [Google Scholar]
  • M. Yagiura, M. Kishida and T. Ibaraki, A 3-flip neighborhood local search for the set covering problem. Eur. J. Oper. Res. 172 (2006) 472–499. [Google Scholar]
  • Y. Yao, C.-H. Chen, B. Abidi, D. Page, A. Koschan and M. Abidi, Sensor planning for automated and persistent object tracking with multiple cameras. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. IEEE (2008). [Google Scholar]
  • H. Zhang, L. Xia, F. Tian, P. Wang, J. Cui, C. Tang, N. Deng and N. Ma, An optimized placement algorithm for collaborative information processing at a wireless camera network. In: 2013 IEEE International Conference on Multimedia and Expo (ICME). IEEE (2013). [Google Scholar]

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