TitleLearning and leveraging context for maritime threat analysis: Vessel classification using Exemplar-SVM
Publication TypeReport
Year of Publication2012
AuthorsMorris, B, Aha, DW, Auslander, B, Gupta, K
InstitutionNaval Research Laboratory, Navy Center for Applied Research in Artificial Intelligence
CityWashington, DC
TypeNCARAI Technical Note
Keywordsmartime images, Object detection and classification
Abstract

Modern fleet security requires accurate threat analysis in real-time, which relies on a range of contextual information (e.g., vessel size, speed, heading, etc.). Rich contextualization may be possible using imaging systems if the images can be used to detect and classify maritime vessels and track their movements. In this work, the effectiveness of the ensemble of Exemplar-SVMs (E-SVM) object detection scheme is evaluated for maritime data where targets are small and have low inter-class variation due to its scalability and ability to learn from limited training examples. Experimental evaluation shows average precision for Annapolis Harbor vessel data is lower than the general 20-category PASCAL VOC challenge due to confusion between boat types.

Refereed DesignationNon-Refereed
Full Text
pdf: 
http://www.nrl.navy.mil/itd/aic/sites/www.nrl.navy.mil.itd.aic/files/pdfs/%28Morris%20et%20al.%2C%20Tech%20Note%202012%209%2028%29%20Learning%20and%20Leveraging%20Context%20for%20MTA%20-%20Vessel%20Classification%20Using%20Exemplar-SVM.pdf
NRL Publication Release Number: 
12-1231-3793
pub_tags: 
Object detection and classification
martime images