Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

Sensors

David Russomanno¹  Srikant Chari² 

[1] Center for Advanced Sensors, Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN, U.S.A. 38152;

关键词: Electronic fence;    imaging sensor;    sparse detector array;    object identification;    Web-service interface;   

DOI  :  10.3390/s8127996

来源: mdpi

【 摘 要 】

This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99%) with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.

【 授权许可】

CC BY   
© 2008 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.