13-11-2012, 02:28 PM
Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks
ABSTRACT
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.