Abstract
Genetic Programming (GP) to evolve programs for
tracking objects quickly in streaming video. A small
number of images, with located objects, are used as
training data and GP automatically performs
feature-selection on these images at the pixel level.
The use of feature functions is introduced, taking a
single offset argument, in contrast to the standard
feature terminal approach. The features include both
'directionless' intensity features and 'directional'
edge detection features. The fitness function rewards
evolved programs that can move training points, located
on a grid around an object, closer to the object. As
such, a good program will also be able to update an
object position from frame to frame for tracking. Two
video sequences are examined, with evolved programs
tracking the left-eye and forehead of a person
successfully. The method is very fast, tracking a frame
in six or seven milliseconds on a 2.6GHz PC.
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