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

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.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Sep 27, 2012
Accession Number
ADA574666

Entities

People

  • Brendan Morris
  • Bryan Auslander
  • David W. Aha
  • Kalyan Gupta

Organizations

  • United States Naval Research Laboratory

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Artificial Intelligence
  • Boats
  • Computer Vision
  • Detection
  • Detectors
  • Education
  • Identification Systems
  • Machine Learning
  • Models
  • Precision
  • Probabilistic Models
  • Security
  • Test And Evaluation
  • Test Methods
  • Test Sets
  • Training

Fields of Study

  • Computer science

Readers

  • Maritime Security/Maritime Homeland Security
  • Neural Network Machine Learning.
  • Systems Analysis and Design