Multi-Sensory Features for Personnel Detection at Border Crossings

Abstract

Personnel detection at border crossings has become an important issue recently. To reduce the number of false alarms, it is important to discriminate between humans and four-legged animals. This paper proposes using enhanced summary autocorrelation patterns for feature extraction from seismic sensors, a multi-stage exemplar selection framework to learn acoustic classifier, and temporal patterns from ultrasonic sensors. We compare the results using decision fusion with Gaussian Mixture Model classifiers and feature fusion with Support Vector Machines. From experimental results, we show that our proposed methods improve the robustness of the system.

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Document Details

Document Type
Technical Report
Publication Date
Jul 08, 2011
Accession Number
ADA564997

Entities

People

  • Mark Hasegawa-johnson
  • Po-sen Huang
  • Thyagaraju Damarla

Organizations

  • University of Illinois Urbana–Champaign

Tags

Communities of Interest

  • Sensors

DTIC Thesaurus Topics

  • Acoustic Signals
  • Acoustic Waves
  • Algorithms
  • Detection
  • Detectors
  • Dimensionality Reduction
  • Doppler Effect
  • Event Detection
  • Feature Extraction
  • Frequency
  • Frequency Shift
  • Information Science
  • Machine Learning
  • Military Research
  • Signal Processing
  • Supervised Machine Learning
  • Waves

Fields of Study

  • Computer science

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Distributed Systems and Data Platform Development
  • Image Processing and Computer Vision.

Technology Areas

  • AI & ML