Title

Autoconfiguration of a dynamic nonoverlapping camera network

Authors

Authors

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

Comments

Authors: contact us about adding a copy of your work at STARS@ucf.edu

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

Share

COinS