Abstract
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Abstract
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We present an automatic vehicle detection system for aerial
surveillance in this paper. In this system, we escape from the stereotype and
existing frameworks of vehicle detection in aerial surveillance, which are
either region based or sliding window based. We design a pixel wise
classification method for vehicle detection. The novelty lies in the fact that,
in spite of performing pixel wise classification, relations among neighboring
pixels in a region are preserved in the feature extraction process. We consider
features including vehicle colors and local features. For vehicle color
extraction, we utilize a color transform to separate vehicle colors and
non-vehicle colors effectively. For edge detection, we apply moment preserving
to adjust the thresholds of the Canny edge detector automatically, which
increases the adaptability and the accuracy for detection in various aerial
images. Afterward, a dynamic Bayesian network (DBN) is constructed for the
classification purpose. We convert regional local features into quantitative
observations that can be referenced when applying pixel wise classification via
DBN. Experiments were conducted on a wide variety of aerial videos. The results
demonstrate flexibility and good generalization abilities of the proposed method
on a challenging data set with aerial surveillance images taken at different
heights and under different camera angles.
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