Title

Online Control Of Active Camera Networks For Computer Vision Tasks

Keywords

3D reconstruction; Active camera networks; Camera control; Computer vision; Motion capture; Surveillance

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). © 2014 ACM 1550-4859/2014/01-ART23 15.00.

Publication Date

1-1-2014

Publication Title

ACM Transactions on Sensor Networks

Volume

10

Issue

2

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/2530283

Socpus ID

84893356771 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84893356771

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