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Accession Number
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ADA564997
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Title
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Multi-Sensory Features for Personnel Detection at Border Crossings.
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Publication Date
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Jul 2011
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Media Count
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9p
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Personal Author
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M. Hasegawa-Johnson P. Huang T. Damarla
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Abstract
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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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Keywords
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Border security
Crossings
Detectors
Feature extraction
Humans
Personnel detection
Seismometers
Senses(Physiology)
Sensor fusion
Ultrasonics
Vector analysis
Warning systems
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Source Agency
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Non Paid ADAS
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NTIS Subject Category
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91I - Emergency Services & Planning
92 - Behavior & Society
62F - Pattern Recognition & Image Processing
63F - Optical Detection
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Corporate Author
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Illinois Univ. at Urbana-Champaign.
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Document Type
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Technical report
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Title Note
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Conference paper.
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NTIS Issue Number
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1304
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Contract Number
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N/A
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