Skip to main content
SpringerLink
Log in

Modeling Coverage in Camera Networks: A Survey

  • Published:
International Journal of Computer Vision Aims and scope Submit manuscript

Abstract

Modeling the coverage of a sensor network is an important step in a number of design and optimization techniques. The nature of vision sensors presents unique challenges in deriving such models for camera networks. A comprehensive survey of geometric and topological coverage models for camera networks from the literature is presented. The models are analyzed and compared in the context of their intended applications, and from this treatment the properties of a hypothetical inclusively general model of each type are derived.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. A maximum resolution constraint is conceivable, e.g., for privacy purposes, but we have not encountered this in the literature.

References

  • Angella, F., Reithler, L.,& Gallesio, F. (2007). Optimal deployment of cameras for video surveillance systems. In Proceedings of IEEE conference on advanced video and signal based surveillance (pp. 388–392).

  • Antone, M.,& Teller, S. (2002). Scalable extrinsic calibration of omni-directional image networks. International Journal of Computer Vision, 49(2/3), 143–174.

    Article  MATH  Google Scholar 

  • Bajramovic, F., Brückner, M.,& Denzler, J. (2009). Using common field of view detection for multi camera calibration. In Proceedings of vision, modeling, and visualization workshop.

  • Bodor, R., Drenner, A., Janssen, M., Schrater, P.,& Papanikolopoulos, N. (2005). Mobile camera positioning to optimize the observability of human activity recognition tasks. In Proceedings of IEEE/RSJ international conference on intelligent robots (pp. 4037–4042).

  • Bodor, R., Drenner, A., Schrater, P.,& Papanikolopoulos, N. (2007). Optimal camera placement for automated surveillance tasks. Journal of Intelligent and Robotic Systems, 50(3), 257–295.

    Article  Google Scholar 

  • Brand, M., Antone, M.,& Teller, S. (2004). Spectral soluction of large-scale extrinsic camera calibration as a graph embedding problem. In Proceedings of 8th European conference on computer vision (pp. 262–273).

  • Brückner, M., Bajramovic, F.,& Denzler, J. (2009). Geometric and probabilistic image dissimilarity measures for common field of view detection. In Proceedings of IEEE computer society conference on computer vision and, pattern recognition (pp. 2052–2057).

  • Cerfontaine, P. A., Schirski, M., Bundgens, D.,& Kuhlen, T. (2006). Automatic multi-camera setup optimization for optical tracking. In Proceedings of virtual reality conference (pp. 295–296).

  • Chen, S.,& Li, Y. (2004). Automatic sensor placement for model-based robot vision. IEEE Transactions on Systems, Man, and Cybernetics, 34(1), 393–408.

    Article  Google Scholar 

  • Chen, T. S., Tsai, H. W., Chen, C. P.,& Peng, J. J. (2010). Object coverage with camera rotation in visual sensor networks. In Proceedings of 6th international wireless communications and mobile computing conference (pp. 79–83).

  • Chen, X.,& Davis, J. (2008). An occlusion metric for selecting robust camera configurations. Machine Vision and Applications, 19(4), 217–222.

    Article  MathSciNet  Google Scholar 

  • Cheng, Z., Devarajan, D.,& Radke, R. J. (2007). Determining vision graphs for distributed camera networks using feature digests. EURASIP Journal on Advances in Signal Processing, 2007, 1–11.

    Article  Google Scholar 

  • Chow, K. Y., Lui, K. S.,& Lam, E. Y. (2007). Achieving 360 angle coverage with minimum transmission cost in visual sensor networks. In Proceedings of IEEE wireless communications and networking conference (pp. 4112–4116).

  • Cowan, C. K.,& Kovesi, P. D. (1988). Automatic sensor placement from vision task requirements. IEEE Transactions on Pattern Analysis and Machine Intelligence, 10(3), 407–416.

    Article  Google Scholar 

  • Dai, R.,& Akyildiz, I. F. (2009). A spatial correlation model for visual information in wireless multimedia sensor networks. IEEE Transactions on Multimedia, 11(6), 1148–1159.

    Article  Google Scholar 

  • Detmold, H., Dick, A. R., Van Den Hengel, A., Cichowski, A., Hill, R., Kocadag, E., Falkner, K.,& Munro, D. S. (2007). Topology estimation for thousand-camera surveillance networks. In Proceedings of 1st ACM/IEEE international conference on distributed smart cameras (pp. 195–202).

  • Detmold, H., Dick, A. R., Van Den Hengel, A., Cichowski, A., Hill, R., Kocadag, E., Yarom, Y., Falkner, K.,& Munro, D. S. (2008). Estimating camera overlap in large and growing networks. In Proceedings of 2nd ACM/IEEE international conference on distributed smart cameras.

  • Devarajan, D.,& Radke, R. J. (2004). Distributed metric calibration of large camera networks. In Proceedings of 1st workshop on broadband advanced sensor networks.

  • Dick, A. R.,& Brooks, M. J. (2004). A stochastic approach to tracking objects across multiple cameras. In Proceedings Australian joint conference on artificial intelligence (pp. 160–170).

  • Ellis, T. J., Makris, D.,& Black, J. K. (2003). Learning a multi-camera topology. In Proceedings of joint IEEE workshop on visual surveillance and performance evaluation of tracking and surveillance (pp. 165–171).

  • Erdem, U. M.,& Sclaroff, S. (2003). Automated placement of cameras in a floorplan to satisfy task-specific constraints. Tech. Report, Boston University.

  • Erdem, U. M.,& Sclaroff, S. (2006). Automated camera layout to satisfy task-specific and floor plan-specific coverage requirements. Computer Vision and Image Understanding, 103(3), 156–169.

    Article  Google Scholar 

  • Farrell, R.,& Davis, L. S. (2008). Decentralized discovery of camera network topology. In Proceedings of 2nd ACM/IEEE international conference on distributed smart cameras.

  • Faugeras, O. (1993). Dimensional computer vision: A geometric viewpoint. London: MIT Press.

    Google Scholar 

  • Fiore, L., Somasundaram, G., Drenner, A.,& Papanikolopoulos, N. (2008). Optimal camera placement with adaptation to dynamic scenes. In Proceedings of IEEE international conference on robotics and automation (pp. 956–961).

  • Gilbert, A.,& Bowden, R. (2006). Tracking objects across cameras by incrementally learning inter-camera colour calibration and patterns of activity. In Proceedings of 9th European conference on computer vision (pp. 125–136).

  • González-Banos, H.,& Latombe, J. C. (2001). A randomized art-gallery algorithm for sensor placement. In Proceedings of 17th annual symposium computational geometry (pp. 232–240).

  • Hill, R., Dick, A. R., Van Den Hengel, A., Cichowski, A.,& Detmold, H. (2008). Empirical evaluation of the exclusion approach to estimating camera overlap. In Proceedings of 2nd ACM/IEEE international conference on distributed smart cameras.

  • Hörster, E.,& Lienhart, R. (2006). On the optimal placement of multiple visual sensors. In Proceedings of 4th ACM international workshop on video surveillance and sensor, networks (pp. 111–120).

  • Hörster, E.,& Lienhart, R. (2009). Optimal placement of multiple visual sensors. In H. Aghajan& A. Cavallaro (Eds.), Multi-camera networks: Principles and applications (Chap. 5, pp. 117–138). Burlington: Academic Press.

  • Huang, C. F., Tseng, Y. C.,& Lo, L. C. (2007). The Coverage Problem in Three-Dimensional Wireless Sensor Networks. J. Interconnection Networks, 8(3), 209–227.

    Article  Google Scholar 

  • Huber, D. F. (2001). Automatic 3D modeling using range images obtained from unknown viewpoints. In Proceedings of 3rd international conference on 3D digital imaging and modeling (Vol. 7, pp. 153–160).

  • Javed, O., Khan, S., Rasheed, Z.,& Shah, M. (2000). Camera handoff: Tracking in multiple uncalibrated stationary cameras. In Proceedings of workshop on human motion (pp. 113–118).

  • Javed, O., Rasheed, Z., Shafique, K.,& Shah, M. (2003). Tracking across multiple cameras with disjoint views. In Proceedings of 9th IEEE international conference on computer vision (pp. 952–957).

  • Jiang, Y., Yang, J., Chen, W.,& Wang, W. (2010). A coverage enhancement method of directional sensor network based on genetic algorithm for occlusion-free surveillance. In Proceedings of international conference on computational aspects of social networks (pp. 311–314).

  • Kang, E. Y., Cohen, I.,& Medioni, G. G. (2000). A graph-based global registration for 2D mosaics integrated media systems center. In Proceedings of international conference on pattern recognition (pp. 257–260).

  • Khan, S.,& Shah, M. (2003). Consistent labeling of tracked objects in multiple cameras with overlapping fields of view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1355–1360.

    Article  Google Scholar 

  • Kulkarni, P., Shenoy, P.,& Ganesan, D. (2007). Approximate initialization of camera sensor networks. In Proceedings of 4th European conference on wireless sensor networks (pp. 67–82).

  • Kurillo, G., Li, Z.,& Bajcsy, R. (2008). Wide-area external multi-camera calibration using vision graphs and virtual calibration object. In Proceedings of 2nd ACM/IEEE international conference on distributed smart cameras.

  • Liu, L., Ma, H.,& Zhang, X. (2008). Analysis for localization-oriented coverage in camera sensor networks. In Proceedings of wireless communications and networking conference (pp. 2579–2584).

  • Liu, L., Ma, H.,& Zhang, X. (2008). On directional K-coverage analysis of randomly deployed camera sensor networks. In Proceedings of IEEE international conference on communications (pp. 2707–2711).

  • Lobaton, E. J., Ahammad, P.,& Sastry, S. S. (2009a). Algebraic approach for recovering topology in distributed camera networks. In Proceedings of ACM/IEEE international conference on information processing in sensor networks.

  • Lobaton, E. J., Sastry, S. S.,& Ahammad, P. (2009b). Building an algebraic topological model of wireless camera networks. In H. Aghajan& A. Cavallero (Eds.), Multi-camera networks: Principles and applications (Chap. 4, pp. 95–115). St. Louis: Academic Press.

  • Lobaton, E. J., Vasudevan, R., Bajcsy, R.,& Sastry, S. (2010). A distributed topological camera network representation for tracking applications. IEEE Transactions on Image Processing, 19(10), 2516–2529.

    Article  MathSciNet  Google Scholar 

  • Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Ma, H.,& Liu, Y. (2005a). Correlation based video processing in video sensor networks. In Proceedings of internaional conference on wireless networks, communications and mobile computing (pp. 987–992).

  • Ma, H.,& Liu, Y. (2005b). On coverage problems of directional sensor networks. In Proceedings of 1st international conference on mobile ad-hoc and sensor networks (pp. 721–731).

  • Ma, H.,& Liu, Y. (2007). Some problems of directional sensor networks. International Journal of Sensor Networks, 2(1–2), 44–52.

    Article  Google Scholar 

  • Ma, Y., Soatto, S., Košecká, J.,& Sastry, S. S. (2004). An invitation to 3-D computer vision. London: Springer.

    Book  Google Scholar 

  • Makris, D., Ellis, T. J.,& Black, J. K. (2004). Bridging the gaps between cameras. In Proceedings of IEEE computer society conference on computer vision and, pattern recognition (pp. 205–210).

  • Malik, R.,& Bajcsy, P. (2008). Automated placement of multiple stereo cameras. In Proceedings of 8th ECCV workshop on omnidirectional vision, camera networks and non-classical cameras.

  • Mandel, Z., Shimsoni, I.,& Keren, D. (2007). Multi-camera topology recovery from coherent motion. In Proceedings of 1st ACM/IEEE international conference distributed smart cameras (pp. 243–250).

  • Marengoni, M., Draper, B. A., Hanson, A.,& Sitaraman, R. (2000). A system to place observers on a polyhedral terrain in polynomial time. Image and Vision Computing, 18(10), 773–780.

    Article  Google Scholar 

  • Marinakis, D.,& Dudek, G. (2005). Topology inference for a vision-based sensor network. In Proceedings of 2nd Canadian conference on computer and robot vision (pp. 121–128).

  • Marinakis, D., Dudek, G.,& Fleet, D. J. (2005). Learning sensor network topology through Monte Carlo expectation maximization. In Proceedings of IEEE International conference on robotics and automation.

  • Maver, J.,& Bajcsy, R. (1993). Occlusions as a guide for planning the next view. IEEE Transactions on Pattern Analysis and Machine Intelligence, 15(5), 417–433.

    Article  Google Scholar 

  • Mavrinac, A.,& Chen, X. (2011). Optimizing load distribution in camera networks with a hypergraph model of camera topology. In Proceedings of 5th ACM/IEEE international conference on distributed smart cameras.

  • Mavrinac, A., Chen, X.,& Tepe, K. (2010). An automatic calibration method for stereo-based 3D distributed smart camera networks. Computer Vision and Image Understanding, 114(8), 952–962.

    Article  Google Scholar 

  • Meguerdichian, S., Koushanfar, F., Potkonjak, M.,& Srivastava, M. B. (2001). Coverage problems in wireless ad-hoc sensor networks. In Proceedings of 20th IEEE international conference on computer communications (pp. 1380–1387).

  • Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., et al. (2006). A comparison of affine region detectors. International Journal of Computer Vision, 65(1), 43–72.

    Article  Google Scholar 

  • Mittal, A. (2006). Generalized multi-sensor planning. In Proceedings of 9th European conference on computer vision.

  • Mittal, A.,& Davis, L.S. (2004). Visibility analysis and sensor planning in dynamic environments. In Proceedings of 8th European conference on computer vision.

  • Mittal, A.,& Davis, L. S. (2008). A general method for sensor planning in multi-sensor systems: Extension to random occlusion. International Journal of Computer Vision, 76(1), 31–52.

    Article  Google Scholar 

  • Moreels, P.,& Perona, P. (2007). Evaluation of features detectors and descriptors based on 3D objects. International Journal of Computer Vision, 73(3), 263–284.

    Article  Google Scholar 

  • Nam, Y., Ryu, J., Choi, Y. J.,& Cho, W. D. (2007). Learning spatio-temporal topology of a multi-camera network by tracking multiple people. Proceedings of World Academy of Science, Engineering and Technology, 24, 175–180.

    Google Scholar 

  • Niu, C.,& Grimson, W. E. L. (2006). Recovering non-overlapping network topology using far-field vehicle tracking data. In Proceedings of 18th international conference on pattern recognition (pp. 944–949).

  • O’Rourke, J. (1987). Art gallery theorems and algorithms. New York: Oxford University Press.

    MATH  Google Scholar 

  • Park, J., Bhat, P. C.,& Kak, A. C. (2006). A look-up table based approach for solving the camera selection problem in large camera networks. In Proceedings of international workshop on distributed smart cameras.

  • Piciarelli, C., Micheloni, C.,& Foresti, G. L. (2009). PTZ camera network reconfiguration. In Proceedings of 3rd ACM/IEEE international conference on distributed smart cameras.

  • Piciarelli, C., Micheloni, C.,& Foresti, G. L. (2010). Occlusion-aware multiple camera reconfiguration. In Proceedings of 4th ACM/IEEE international conference on distributed smart cameras.

  • Qian, C.,& Qi, H. (2008). Coverage estimation in the presence of occlusions for visual sensor networks. In S. Nikoletseas, B. Chlebus, D. Johnson,& B. Krishnamachari (Eds.), Distributed computing in sensor systems (pp. 346–356). Berlin: Springer.

    Chapter  Google Scholar 

  • Ram, S., Ramakrishnan, K. R., Atrey, P. K., Singh, V. K.,& Kankanhalli, M. S. (2006). A design methodology for selection and placement of sensors in multimedia surveillance systems. In Proceedings of 4th ACM international workshop on video surveillance and sensor networks (pp. 121–130).

  • Reed, M. K.,& Allen, P. K. (2000). Constraint-based sensor planning for scene modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(12), 1460–1467.

    Article  Google Scholar 

  • Sawhney, H. S., Hsu, S.,& Kumar, R. (1998). Robust video mosaicing through topology inference and local to global alignment. In Proceedings of 5th European conference on computer vision (pp. 103–119).

  • Sharp, G. C., Lee, S. W.,& Wehe, D. K. (2004). Multiview registration of 3D scenes by minimizing error between coordinate frames. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(8), 1037–1050.

    Article  Google Scholar 

  • Shen, C., Zhang, C.,& Fels, S. (2007). A multi-camera surveillance system that estimates quality-of-view measurement. In Proceedings of IEEE international conference on image processing (pp. 193–196).

  • Song, B., Kamal, A. T., Soto, C., Ding, C., Farrell, J. A.,& Roy-Chowdhury, A. K. (2010). Tracking and activity recognition through consensus in distributed camera networks. IEEE Transactions on Image Processing, 19(10), 2564–2579.

    Article  MathSciNet  Google Scholar 

  • Soro, S.,& Heinzelman, W. B. (2007). Camera selection in visual sensor networks. In Proceedings of IEEE conference on advanced video and signal based surveillance (pp. 81–86).

  • Stauffer, C. (2003). Estimating tracking sources and sinks. In Proceedings of IEEE Computer Society conference on computer vision and pattern recognition (Vol. 4).

  • Stauffer, C. (2005). Learning to track objects through unobserved regions. In Proceedings of IEEE Computer Society workshop on motion and video computing (pp. 96–102).

  • Stauffer, C.,& Tieu, K. (2003). Automated multi-camera planar tracking correspondence modeling. In Proceedings of IEEE Computer Society conference on computer vision and, pattern recognition (pp. 259–266).

  • Tao, D., Ma, H.,& Liu, L. (2006). Coverage-enhancing algorithm for directional sensor networks. In Proceedings of 2nd international conference on mobile ad-hoc and sensor networks (pp. 256–267).

  • Tarabanis, K. A., Allen, P. K.,& Tsai, R. Y. (1995). A survey of sensor planning in computer vision. IEEE Transactions on Robotics and Automation, 11(1), 86–104.

    Article  Google Scholar 

  • Tarabanis, K. A., Tsai, R. Y.,& Allen, P. K. (1994). Analytical characterization of the feature detectability constraints of resolution, focus, and field-of-view for vision sensor planning. CVGIP: Image Understanding, 59(3), 340–358.

    Article  Google Scholar 

  • Tarabanis, K. A., Tsai, R. Y.,& Allen, P. K. (1995). The MVP sensor planning system for robotic vision tasks. IEEE Transactions on Robotics and Automation, 11(1), 72–85.

    Article  Google Scholar 

  • Tarabanis, K. A., Tsai, R. Y.,& Kaul, A. (1996). Computing occlusion-free viewpoints. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18(3), 279–292.

    Article  Google Scholar 

  • Tieu, K., Dalley, G.,& Grimson, W. E. L. (2005). Inference of non-overlapping camera network topology by measuring statistical dependence. In Proceedings of 10th IEEE international conference on computer vision (pp. 1842–1849).

  • Van Den Hengel, A., Dick, A. R., Detmold, H., Cichowski, A.,& Hill, R. (2007). Finding camera overlap in large surveillance networks. In Proc. 8th Asian conference on computer vision (pp. 375–384).

  • Van Den Hengel, A., Dick, A. R.,& Hill, R. (2006). Activity topology estimation for large networks of cameras. In Proceedings of IEEE international conference on advanced video and signal based surveillance.

  • Wang, B. (2010). Coverage control in sensor networks. Berlin: Springer.

    Book  MATH  Google Scholar 

  • Wang, C., Qi, F.,& Shi, G. M. (2009). Nodes placement for optimizing coverage of visual sensor networks. In P. Muneesawang, F. Wu, I. Kumazawa, A. Roeksabutr, M. Liao,& X. Tang (Eds.), Advances in Multimedia Information Processing—PCM 2009 (pp. 1144–1149). Berlin: Springer.

    Chapter  Google Scholar 

  • Yao, Y., Chen, C. H., Abidi, B., Page, D., Koschan, A.,& Abidi, M. A. (2008). Sensor planning for automated and persistent object tracking with multiple cameras. In Proceedings of IEEE Computer Society conference on computer vision and, pattern recognition (pp. 1–8).

  • Zhao, J., Cheung, S. C.,& Nguyen, T. (2008). Optimal camera network configurations for visual tagging. Journal of Selected Topics in Signal Processing, 2(4), 464–479.

    Article  Google Scholar 

  • Zhao, J., Cheung, S. C.,& Nguyen, T. (2009). Optimal visual sensor network configuration. In H. Aghajan& A. Cavallaro (Eds.), Multi-camera networks: Principles and applications (Chap. 6, pp. 139–162). Burlington: Academic Press.

  • Zou, X., Bhanu, B., Song, B.,& Roy-Chowdhury, A. K. (2007). Determining topology in a distributed camera network. In Proceedings of IEEE international conference on image processing (pp. 133–136).

Download references

Acknowledgments

This research was supported in part by the Natural Sciences and Engineering Research Council of Canada.

Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada

    Aaron Mavrinac & Xiang Chen

Authors
  1. Aaron Mavrinac
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiang Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mavrinac, A., Chen, X. Modeling Coverage in Camera Networks: A Survey. Int J Comput Vis 101, 205–226 (2013). https://doi.org/10.1007/s11263-012-0587-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11263-012-0587-7

Keywords

Search

Navigation