Sparse detector sensor model

Robinson, Andrew; Halford, Carl E.; Perry, Edward H.; Wyatt, Thomas E.

doi:10.1117/12.780106

Cited by 2 publications

(4 citation statements)

References 11 publications

(4 reference statements)

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“…As modeled in Robinson et al [8], the sparse detector system consists of an array of sensing elements deployed in a vertical configuration on a transmitting/receiving platform. The sensor system also has a reflecting platform that is placed at a distance represented by width sys .…”

Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

“…Robinson et al [8] have developed and used this model for rough trade-off analyses, which include the effects of the optics, atmosphere, detectors, object-of-interest characteristics, and system characteristics, such as detector pitch and normalized detectivity. As further described in [8], the sensor can be classified as either having a staring or a scanned system type [9].…”

Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

“…As further described in [8], the sensor can be classified as either having a staring or a scanned system type [9]. Since it consists of a stationary sparse detector array with no device for scanning the image across the detectors it could be viewed as a staring system in which the vertical resolution is dependent upon the deployment of the individual sensing elements, which comprise the overall device.…”

Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

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Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

Russomanno

Chari

Halford

2008

Sensors

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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.

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Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

Section: Sparse Detector Imaging Sensormentioning

confidence: 99%

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Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

Russomanno

Chari

Halford

2008

Sensors

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“…We decided on a passive-IR approach as a viable alternative. [5][6][7] Pyroelectric-IR (PIR) devices were an obvious choice for detectors because they need no cryogenic cooling and exist in both single-device and array form. To help find the best configuration, we developed a detailed video simulation that produces output from arbitrary configurations of PIR arrays.…”

mentioning

confidence: 99%

Profiling sensors for border and perimeter security

Jacobs¹

2009

SPIE Newsroom

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A novel intrusion detector offers several advantages compared with traditional methods, including a simpler design and improved levels of discrimination.Border and perimeter sensing is key to addressing concerns about national security and criminal activity. In addition, due to the vastness of national borders and the human resources needed to patrol even modest-sized areas, detection systems must be automated. Finally, for environmental and operational reasons, sensor systems must be able to communicate at low power or bandwidth, and to keep false alarms to a minimum.Simple pyroelectric motion detectors, such as those used in automatic light and home security systems, meet many of these requirements but produce too many false alarms. Imaging sensors provide a high level of discrimination, but they typically require human interaction as well as transmission of the image, which is costly in terms of bandwidth. We have been pursuing an approach that lies somewhere in between motion detectors and imaging sensors. Using a small and sparse array of detectors, we sense a minimal set of features from a moving object that enables reliable discrimination of humans, animals, and vehicles.Our approach originated with a concept put forward by Ron Sartain of the US Army Research Laboratory. 1 He reasoned that useful information about traffic along a path could be derived simply by determining the profile or silhouette of the objects that traverse it. It was hoped as well that critical discrimination could be obtained automatically. Prior efforts had shown that a great deal of information was available from shape alone. 2 To investigate this idea further, we constructed a simple sensor consisting of two vertical posts populated with a sparse array of near-IR transmitters and receivers 3, 4 (see Figure 1) that produces a series of parallel near-IR beams between the posts. As an object passes through, each part of it breaks one of the beams. Over time, the output of all the transmitters and receivers forms a crude image representing the profile of the object. This device was used to collect profiles of people, animals, and other objects. Several straightforward classification algorithms were then trained, tested, and evaluated using the data. We achieved better than 95% correct classification of our major categories.The near-IR beam prototype proved the concept of a profiling sensor, but it has many drawbacks in terms of deployment. We decided on a passive-IR approach as a viable alternative. [5][6][7] Pyroelectric-IR (PIR) devices were an obvious choice for detectors because they need no cryogenic cooling and exist in both single-device and array form. To help find the best configuration, we developed a detailed video simulation that produces output from arbitrary configurations of PIR arrays. We also used the simulation to account for the effects of lenses or mirrors for focusing. Figure 2 presents an example output.We are currently testing a prototype sensor constructed by the US Army Night Vision and Electronics Sensors Dir...

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Sparse detector sensor model

Cited by 2 publications

References 11 publications

Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

Profiling sensors for border and perimeter security

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