Paper Info
Title
Vehicle Detection on Aerial Images by Extracting Corner Features for Rotational Invariant Shape Matching
Abstract
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.
Year
DOI
Venue
2011
10.1109/CIT.2011.56
CIT
Keywords
Field
DocType
intelligent transportation system,aerial images,rotational invariant shape matching,corner feature,shadow detection,important component,vehicle detection,low resolution,difficult problem,effective vehicle detection method,extracting corner features,aerial image,benchmark method,edge detection,shape,feature extraction,image fusion,image segmentation
Shadow,Computer vision,Object detection,Image fusion,Pattern recognition,Computer science,Vehicle detection,Image segmentation,Feature extraction,Invariant (mathematics),Artificial intelligence,Intelligent transportation system
Conference
Citations 
PageRank 
References 
2
0.49
8
Authors
1
Name
Order
Citations
PageRank
Wang Sheng185.80