Accession Number:

ADA574666

Title:

Learning and Leveraging Context for Maritime Threat Analysis: Vessel Classification using Exemplar-SVM

Descriptive Note:

Corporate Author:

NAVAL RESEARCH LAB WASHINGTON DC NAVY CENTER FOR APPLIED RESEARCH IN ARTIFICIAL INTELLIGENCE

Report Date:

2012-09-27

Pagination or Media Count:

29.0

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.

Subject Categories:

  • Cybernetics
  • Marine Engineering

Distribution Statement:

APPROVED FOR PUBLIC RELEASE