One of the challenges of using cameras
to give computers a way to see is finding a way to capture information
One approach is to, like biological stereo vision, triangulate
from a pair of camera positions. There are practical drawbacks of this
approach, however: double the camera equipment, double the power needs,
and tricky calibration.
Researchers from Clemson University and the University of Florida
have improved a method of determining depth information using a single
The advance allows a single camera to detect the speed of objects
and could be used anywhere robotic vision is needed. The method promises
to decrease the cost of robotic vision and enable vision for applications
like tiny flying robots.
It could also be used in place of a radar detector, according
to the researchers. Unlike a radar detector, the camera could not be detected
by motorists because it does not emit energy.
The camera calculates three-dimensional information from two-dimensional
images based on three aspects of an object's movement: speed, acceleration
and jerk. Jerk is, as the name implies, the smoothness of the motion.
The method uses successive images to calculate an object's speed, and
requires that the size of the object be estimated based on the distance
between two parts of an object -- such as the wingtips of an airplane.
The researchers came up with the method by combining recent advances
in image processing geometry and nonlinear mathematics. Nonlinear systems
have outputs that are not proportional to the system's inputs. Previous
methods of calculating the speed of an object using a single camera used
the Kalman Filter, a technique that simplifies equations that describe
the object and its motion. The researchers' advance replaces the Kalman
Filter with a nonlinear estimation technique that does not simplify the
underlying mathematics, according to the researchers.
The work appeared in the March 2005 issue of Automatica
(Indication of a Moving Object's Velocity with a Fixed Camera).
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