skip to main content
10.1145/1099396.1099421acmconferencesArticle/Chapter ViewAbstractPublication PagesmmConference Proceedingsconference-collections
Article

Constructing task visibility intervals for a surveillance system

Published: 11 November 2005 Publication History

Abstract

One of the goals of a multi-camera surveillance system is to collect useful video clips of objects in the scene. Objects in the collected videos should be unobstructed, in the field of view of the given camera, and meet task-specific resolution requirement. For this purpose, we describe an algorithm that constructs "task visibility intervals", which are tuples of information about what to sense (task-object pairs), when to sense (feasible future temporal intervals to start a task) and how to sense (the camera to use and the corresponding viewing angles and focal length). The algorithm first looks for temporal intervals within which the angular extents of objects overlap each other, causing the object farthest from the given camera to be occluded. Outside these intervals, sub-intervals are then constructed such that feasible camera settings exist for capturing the object. Experimental results are provided to illustrate the system capabilities in constructing such task visibility intervals, followed by scheduling them using a greedy algorithm.

References

[1]
ABRAMS, S., ALLEN, P. K., AND TARABANIS, K. Computing camera viewpoints in an active robot work cell. International Journal of Robotics Research 18, 2 (February 1999).
[2]
ABRAMS, S., ALLEN, P. K., AND TARABANIS, K. A. Dynamic sensor planning. In ICRA (2) (1993), pp. 605--610.
[3]
COMANICIU, D., RAMESH, V., AND MEER, P. Kernel-based object tracking. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 5 (May 2003).
[4]
COWAN, C. K., AND KOVESI, P. D. Automatic sensor placement from vision task requirement. IEEE Transactions on Pattern Analysis and machine intelligence 10, 3 (1988), 407--416.
[5]
HARTLEY, R. I., AND ZISSERMAN, A. Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521623049, 2000.
[6]
KUTULAKOS, K. Affine surface reconstruction by purposive viewpoint control. In International Conference on Computer Vision, Boston, Massachussetts, USA (June 1995).
[7]
KUTULAKOS, K., AND DYER, C. R. Global surface reconstruction by purposive control of observer motion. In IEEE Conference on Computer Vision and Pattern Recognition, Seattle, Washington, USA (June 1994).
[8]
KUTULAKOS, K., AND DYER, C. R. Occluding contour detection using affine invariants and purposive viewpoint control. In IEEE Conference on Computer Vision and Pattern Recognition, Seattle, Washington, USA (June 1994).
[9]
KUTULAKOS, K., AND DYER, C. R. Recovering shape by purposive viewpoint adjustment. International Journal of Computer Vision 12, 2 (1994), 113--136.
[10]
MITTAL, A., AND DAVIS, L. S. Visibility analysis and sensor planning in dynamic environments. In European Conference on Computer Vision (May 2004).
[11]
STAMOS, I., AND ALLEN, P. Interactive sensor planning. In Computer Vision and Pattern Recognition Conference (Jun 1998), pp. 489--494.
[12]
TARABANIS, K., ALLEN, P., AND TSAI, R. A survey of sensor planning in computer vision. IEEE Transactions on Robotics and Automation 11, 1 (1995), 86--104.
[13]
TARABANIS, K., TSAI, R., AND ALLEN, P. The mvp sensor planning system for robotic vision tasks. IEEE Transactions on Robotics and Automation 11, 1 (February 1995), 72--85.
[14]
YI, S. K., HARALICK, R. M., AND SHAPIRO, L. G. Optimal sensor and light-source positioning for machine vision. Computer Vision and Image Understanding 61, 1 (1995), 122--137.

Cited By

View all
  • (2014)Online control of active camera networks for computer vision tasksACM Transactions on Sensor Networks (TOSN)10.1145/253028310:2(1-40)Online publication date: 31-Jan-2014
  • (2007)Task Scheduling in Large Camera NetworksComputer Vision – ACCV 200710.1007/978-3-540-76386-4_37(397-407)Online publication date: 2007

Index Terms

  1. Constructing task visibility intervals for a surveillance system

    Recommendations

    Comments

    Information & Contributors

    Information

    Published In

    cover image ACM Conferences
    VSSN '05: Proceedings of the third ACM international workshop on Video surveillance & sensor networks
    November 2005
    168 pages
    ISBN:1595932429
    DOI:10.1145/1099396
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

    Sponsors

    Publisher

    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 11 November 2005

    Permissions

    Request permissions for this article.

    Check for updates

    Author Tag

    1. sensor scheduling

    Qualifiers

    • Article

    Conference

    MM&Sec '05
    MM&Sec '05: Multimedia and Security Workshop 2005
    November 11, 2005
    Hilton, Singapore

    Contributors

    Other Metrics

    Bibliometrics & Citations

    Bibliometrics

    Article Metrics

    • Downloads (Last 12 months)2
    • Downloads (Last 6 weeks)0
    Reflects downloads up to 15 Feb 2025

    Other Metrics

    Citations

    Cited By

    View all
    • (2014)Online control of active camera networks for computer vision tasksACM Transactions on Sensor Networks (TOSN)10.1145/253028310:2(1-40)Online publication date: 31-Jan-2014
    • (2007)Task Scheduling in Large Camera NetworksComputer Vision – ACCV 200710.1007/978-3-540-76386-4_37(397-407)Online publication date: 2007

    View Options

    Login options

    View options

    PDF

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader

    Figures

    Tables

    Media

    Share

    Share

    Share this Publication link

    Share on social media