NDLI: Sensor fusion and feature-based human/animal classification for Unattended Ground Sensors

Content Provider	IEEE Xplore Digital Library
Author	Narayanaswami, R. Gandhe, A. Tyurina, A. Mehra, R.K.
Copyright Year	2010
Description	Author affiliation: Scientific Systems Company Inc. (SSCI), 500 W. Cummings Park, Suite 3000, Woburn, MA 01801-6562 (Narayanaswami, R.; Gandhe, A.; Tyurina, A.; Mehra, R.K.)
Abstract	In this paper we examine novel signal processing algorithms that utilize wavelet statistics, spectral statistics and power spectral density in addition to cadence and kurtosis for robust discrimination of humans and animals in an Unattended Ground Sensor (UGS) field. The wavelet statistics are based on the average, variance and energy of the third scale residue. The spectral statistics are based on amplitude and shape features. A learning classifier approach is used for discrimination. Training data consists of scripted events with humans walking/running along known paths; as well as riders on horses and moving vehicles on a two node sensor network. Natural events are recorded when animals, such as cows, coyotes, rabbits and kangaroo rats are in the vicinity of the sensor nodes. Each node has a three axis accelerometer and a three axis geophone and one node has a low frequency geophone in addition. In our work we use the C4.5 classifier which is a tree-based classifier and is capable of modeling complex decision surfaces while simultaneously limiting the complexity of the trees through pruning schemes. The classifier is tested on test data and the performance results are very promising — results indicate that UGS-only systems are indeed feasible for border security. The development of a successful signal processing solution to better discriminate between humans and animals would be very valuable to the Department of Homeland Security and our paper will summarize these new results.
Starting Page	344
Ending Page	350
File Size	946763
Page Count	7
File Format	PDF
ISBN	9781424460472
e-ISBN	9781424460489
DOI	10.1109/THS.2010.5655025
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-11-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Legged locomotion border security Shape learning classifier Humans Unattended Ground Sesnors accelerometer Horses human animal discrimination Time frequency analysis geophone Classification tree analysis
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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