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Sparse Detector Imaging Sensor with Two-Class Silhouette Classification
AbstractThis 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.
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MDPI and ACS Style
Russomanno, D.; Chari, S.; Halford, C. Sparse Detector Imaging Sensor with Two-Class Silhouette Classification. Sensors 2008, 8, 7996-8015.View more citation formats
Russomanno D, Chari S, Halford C. Sparse Detector Imaging Sensor with Two-Class Silhouette Classification. Sensors. 2008; 8(12):7996-8015.Chicago/Turabian Style
Russomanno, David; Chari, Srikant; Halford, Carl. 2008. "Sparse Detector Imaging Sensor with Two-Class Silhouette Classification." Sensors 8, no. 12: 7996-8015.