Digital Signal Processing Reference
In-Depth Information
Distributed Smart Cameras and Distributed
Computer Vision
Marilyn Wolf and Jason Schlessman
Abstract
Distributed smart cameras are multiple-camera systems that perform
computer vision tasks using distributed algorithms. Distributed algorithms scale
better to large networks of cameras than do centralized algorithms. However, new
approaches are required to many computer vision tasks in order to create efficient
distributed algorithms. This chapter motivates the need for distributed computer
vision, surveys background material in traditional computer vision, and describes
several distributed computer vision algorithms for calibration, tracking, and gesture
recognition.
1
Introduction
Distributed smart cameras
have emerged as an important category of distributed
sensor and signal processing systems. Distributed sensors in other media have been
important for quite some time, but recent advances have made the deployment of
large camera systems feasible. The unique properties of imaging add new classes of
problems that are not apparent in unidimensional and low-rate sensors.
Physically
distributed cameras
have been used in computer vision for quite some time to han-
dle two problems:
occlusion
and
pixels-on-target
. Cameras at different locations
expose and occlude different parts of the scene. Their imagery can be combined to
create a more complete model of the scene. Pixels-on-target refers to the resolution
with which a part of the scene is captured, which in most applications is primarily
limited by sensor resolution and not by optics. Wide-angle lenses cover a large area
M. Wolf (
)
School of Electrical and Computer Engineering, Georgia Institute of Technology
e-mail:
wolf@ece.gatech.edu
J. Schlessman
Department of Electrical Engineering, Princeton University
e-mail:
jschlessman@gmail.com
