Abstract
This paper describes an object matching system which is able to
extract objects of interest from outdoor scenes and match them. Our
application (in the domain of IVHS) involves measuring the average
travel time in a road network. The extraction of the object of interest
is performed by fusing multiple cues including motion, color, edges, and
model information. Two objects extracted from images captured by two
independent cameras at different times are then matched to evaluate
their similarity. Color indexing based on histogram matching is used to
avoid matching all possible pairs of objects. To resolve ambiguities,
further matching is done by measuring the Hausdorff distance between two
sets of edge points. The object matching system was given 2 sets of 40
vehicles. It was able to identify 23 of the 30 correct matches and all
the false matches were rejected. Color indexing reduced the number of
candidates for a match from 40 to 2. This matching accuracy is adequate
to obtain a reliable estimate of the average travel time
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