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Car Detection in Low Resolution Aerial Images
Tao Zhao and Ram Nevatia
{taozhao|nevatia} (at) iris (o) usc (o) edu
ICCV 2001 (International Conference on Computer Vision), Vancouver, British Columbia, Canada, July, 2001.
Image and Vision Computing Journal, to appear.
Abstract
We present a system to detect passenger cars in aerial images where cars appear as small objects. We pose this as a
3D object recognition problem to account for the variation
in viewpoint and the shadow. We started from psychological tests to nd important features for human detection of
cars. Based on these observations, we selected the boundary of the car body, the boundary of the front windshield,
and the shadow as the features. Some of these features
are a ected by the intensity of the car and whether or
not there is a shadow along it. This information is represented in the structure of the Bayesian network that
we use to integrate all features. Experiments show very
promising results even on some very challenging images.
Car Model and Features
A wireframe car model is used and the features used are: 4 boundary sides (in red), 4 front windshield sides (in blue), (at most) two shadow boundary sides (in green) and intensity of the shadow region.
Feature Integration
The features are collected from the image and integrated using a Bayesian network with the following structure. Car intensity and
the shadow beside the feature are two enviornmental variables measured from the image or predicted by sun angle. The parameters of
the Bayesian network is learnt from examples.
Selected Results
Washington DC Dataset
Fort Hood Dataset
Download PDF file of the paper (conference version).
Download PDF file of the paper (journal version).
