Autoconfiguration of a dynamic nonoverlapping camera network

    Authors

    I. N. Junejo; X. C. Cao;H. Foroosh

    Comments

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    Abbreviated Journal Title

    IEEE Trans. Syst. Man Cybern. Part B-Cybern.

    Keywords

    image of the absolute conic (IAC); nonoverlapping; camera network; self-calibration; vanishing line; SELF-CALIBRATION; POSE ESTIMATION; ADAPTIVE TRACKING; IMAGE SEQUENCES; MOBILE ROBOT; AUTOCALIBRATION; NAVIGATION; RECONSTRUCTION; ENVIRONMENTS; PARAMETERS; Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Cybernetics

    Abstract

    In order to monitor sufficiently large areas of interest for surveillance or any event detection, we need to look beyond stationary cameras and employ an automatically configurable network of nonoverlapping cameras. These cameras need not have an overlapping field of view and should be allowed to move freely in space. Moreover, features like zooming in/out, readily available in security cameras these days, should be exploited in order to focus on any particular area of interest if needed. In this paper, a practical framework is proposed to self-calibrate dynamically moving and zooming cameras and determine their absolute and relative orientations, assuming that their relative position is known. A global linear solution is presented for self-calibrating each zooming/focusing camera in the network. After self-calibration, it is shown that only one automatically computed vanishing point and a line lying on any plane orthogonal to the vertical direction is sufficient to infer the dynamic network configuration. Our method generalizes previous work which considers restricted camera motions. Using minimal assumptions, we are able to successfully demonstrate promising results on synthetic, as well as on real data.

    Journal Title

    Ieee Transactions on Systems Man and Cybernetics Part B-Cybernetics

    Volume

    37

    Issue/Number

    4

    Publication Date

    1-1-2007

    Document Type

    Article

    Language

    English

    First Page

    803

    Last Page

    816

    WOS Identifier

    WOS:000247833000005

    ISSN

    1083-4419

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