Multi-sensory features for personnel detection at border crossings

Po Sen Huang, Thyagaraju Damarla, Mark Allan Hasegawa-Johnson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationFusion 2011 - 14th International Conference on Information Fusion
StatePublished - 2011
Event14th International Conference on Information Fusion, Fusion 2011 - Chicago, IL, United States
Duration: Jul 5 2011Jul 8 2011

Other

Other14th International Conference on Information Fusion, Fusion 2011
CountryUnited States
CityChicago, IL
Period7/5/117/8/11

Keywords

  • Footstep detection
  • Gaussian mixture models
  • Personnel detection
  • Sensor fusion
  • Support vector machines

ASJC Scopus subject areas

  • Information Systems

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  • Cite this

    Huang, P. S., Damarla, T., & Hasegawa-Johnson, M. A. (2011). Multi-sensory features for personnel detection at border crossings. In Fusion 2011 - 14th International Conference on Information Fusion [5977673]