The various asymmetrical threats in the urban environment have driven the need for persistent surveillance
and methods to exploit the data provided by passive sensing platforms. The primary goal is to track vehicles
as they move through the urban environment. The rather large number of ambiguous tracking events requires
incorporation of target features to maintain track purity. This paper will discuss a feature extraction technique
that will be referred to as "feature-aided" tracking to mitigate some of the tracking issues in this environment (e.g.
rotation and illumination invariance, partial occlusion, and
move-stop-move transitions). The feature extraction
method applied is loosely based on the SPIN histogram method of applying a two-dimensional histogram relative
to the center of an object. This paper focuses on applying a simplified version of the intensity-based two-dimensional
histogram and gradient-based two-dimensional histogram introduced by the works of Mikolajczyk
and Schmid, and Lazebnik, Schmid, and Ponce. Instead of applying the matching technique on a still frame
subjected to various image transformations, we will apply this technique to sequential frames of imagery in an
urban environment. This approach is intended to be the first of several steps towards eventually integrating a
feature-aided tracking option as one of multiple sources of measurement association. The preliminary results
show potential signs of success especially with rotation-invariance and move-stop-move transitions; however,
additional efforts are required associated with illumination invariance, partial occlusion and disambiguation of
close proximity objects.
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