Vehicle Detection on Aerial Images by Extracting Corner Features for Rotational Invariant Shape Matching

Vehicle detection from aerial images has been extensively studied in many research papers and it is an important component of an intelligent transportation system. In the meantime, it is still a difficult problem with many open questions due to challenges caused by various factors such as low resolution of the aerial images, features restricted to a particular type of car, noise from other objects or object shadows, and occulsion in urban environments. By investigating several benchmark methods and frameworks in the literature, this paper proposes a novel feature fusion framework which successfully implements an effective vehicle detection method based on shadow detection followed by a rotational invariant shape matching of corner features. Promising results are obtained from the experiments.