Title

Online Control of Active Camera Networks for Computer Vision Tasks

Authors

Authors

A. Ilie;G. Welch

Comments

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

Abbreviated Journal Title

ACM Trans. Sens. Netw.

Keywords

Algorithms; Experimentation; Measurement; Performance; Camera control; active camera networks; computer vision; surveillance; motion capture; 3D reconstruction; VISUAL OBJECT TRACKING; STATE ESTIMATION; SURVEILLANCE; SYSTEMS; Computer Science, Information Systems; Telecommunications

Abstract

Large networks of cameras have been increasingly employed to capture dynamic events for tasks such as surveillance and training. When using active cameras to capture events distributed throughout a large area, human control becomes impractical and unreliable. This has led to the development of automated approaches for online camera control. We introduce a new automated camera control approach that consists of a stochastic performancemetric and a constrained optimizationmethod. The metric quantifies the uncertainty in the state of multiple points on each target. It uses state-space methods with stochastic models of target dynamics and camera measurements. It can account for occlusions, accommodate requirements specific to the algorithms used to process the images, and incorporate other factors that can affect their results. The optimization explores the space of camera configurations over time under constraints associated with the cameras, the predicted target trajectories, and the image processing algorithms. The approach can be applied to conventional surveillance tasks (e. g., tracking or face recognition), as well as tasks employing more complex computer vision methods (e. g., markerless motion capture or 3D reconstruction).

Journal Title

Acm Transactions on Sensor Networks

Volume

10

Issue/Number

2

Publication Date

1-1-2014

Document Type

Article

Language

English

First Page

40

WOS Identifier

WOS:000330625100008

ISSN

1550-4859

Share

COinS