Orientation aware vehicle detection in aerial images

Vehicle detection in aerial images is of great interest in the field of remote sensing. Many methods such as the sliding-window-based detection have been successfully developed. A simple and effective mechanism to improve the existing methods is proposed. Vehicle in aerial images usually appears in arbitrary directions. Previous algorithms need to repeat the search at a pixel with all the possible orientations, which often bring the problem of increasing false alarms and computational complexity. To solve this problem, image local orientation is introduced into detection that provides a proper search direction for each pixel. Experimental results on a public database, unmanned aerial vehicle (UAV) and satellite images demonstrate the effectiveness and promising improvements in comparison with existing techniques.

[1]  Frédéric Jurie,et al.  Vehicle detection in aerial imagery : A small target detection benchmark , 2016, J. Vis. Commun. Image Represent..

[2]  Horst Bischof,et al.  On-line boosting-based car detection from aerial images , 2008 .

[3]  P. Milanfar,et al.  Multiscale principal components analysis for image local orientation estimation , 2002, Conference Record of the Thirty-Sixth Asilomar Conference on Signals, Systems and Computers, 2002..

[4]  Larry S. Davis,et al.  Vehicle Detection Using Partial Least Squares , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Hamid Soltanian-Zadeh,et al.  Radon transform orientation estimation for rotation invariant texture analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Ramakant Nevatia,et al.  Car detection in low resolution aerial image , 2001, Proceedings Eighth IEEE International Conference on Computer Vision. ICCV 2001.

[7]  Farid Melgani,et al.  Detecting Cars in UAV Images With a Catalog-Based Approach , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Jae-Young Choi,et al.  Vehicle Detection from Aerial Images Using Local Shape Information , 2009, PSIVT.