Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 52, Issue 10, Pages 6356-6367Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2013.2296351
Keywords
Car detection; feature extraction; histogram of gradient (HoG); support vector machine (SVM); unmanned aerial vehicle (UAV)
Ask authors/readers for more resources
This paper presents a new method for the automatic detection of cars in unmanned aerial vehicle (UAV) images acquired over urban contexts. UAV images are characterized by an extremely high spatial resolution, which makes the detection of cars particularly challenging. The proposed method starts with a screening operation in which the asphalted areas are identified in order to make the car detection process faster and more robust. Subsequently, filtering operations in the horizontal and vertical directions are performed to extract histogram-of-gradient features and to yield a preliminary detection of cars after the computation of a similarity measure with a catalog of cars used as reference. Three different strategies for computing the similarity are investigated. Successively, for the image points identified as potential cars, an orientation value is computed by searching for the highest similarity value in 36 possible directions. The last step is devoted to the merging of the points which belong to the same car because it is likely that a car is identified by more than one point due to the extremely high resolution of UAV images. As outcomes, the proposed method provides the number of cars in the image, as well as the position and orientation for each of them. Interesting experimental results, conducted on a set of real UAV images acquired over an urban area, are presented and discussed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available