Ground Vehicle Classification Using Hidden Markov Models

Abstract

Ground vehicle classification is performed using hidden Markov modelling of cepstral coefficients. The hidden Markov model (HMM) is used to represent audio signals. These signals are obtained as the vehicles travel past audio sensor arrays. Well known HMM training algorithms are applied to train models from training data. The trained models are used in two classification rules: the MAP rule, and a list-based rule due to Forney. Under some general assumptions, these approaches can be regarded as optimal. Using recordings from the ACID database, over 96% recognition rate on single vehicle classification is achieved.

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Document Details

Document Type
Technical Report
Publication Date
Oct 04, 2001
Accession Number
ADA409368

Entities

People

  • Howard W. Sabrin
  • William J. Roberts
  • Yariv Ephraim

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Abstracts
  • Battlefields
  • Classification
  • Electric Fields
  • Ground Vehicles
  • Hidden Markov Models
  • Markov Models
  • Models
  • Night Vision
  • Training
  • Vehicles

Fields of Study

  • Engineering

Readers

  • Acoustical Oceanography.
  • Neural Network Machine Learning.