Accession Number:

ADA564997

Title:

Multi-Sensory Features for Personnel Detection at Border Crossings

Descriptive Note:

Conference paper

Corporate Author:

ILLINOIS UNIV AT URBANA-CHAMPAIGN

Personal Author(s):

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

Report Date:

2011-07-08

Pagination or Media Count:

9.0

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.

Descriptors:

• *BORDER SECURITY
• *PERSONNEL DETECTION
• CROSSINGS
• DETECTORS
• FEATURE EXTRACTION
• HUMANS
• SEISMOMETERS
• SENSES(PHYSIOLOGY)
• SENSOR FUSION
• ULTRASONICS
• VECTOR ANALYSIS
• WARNING SYSTEMS

Subject Categories:

• Civil Defense
• Miscellaneous Detection and Detectors

Distribution Statement:

APPROVED FOR PUBLIC RELEASE