Near-IR Sparse Detector Sensor for Intelligent Electronic Fence Applications

Russomanno, David J.; Chari, Srikant; Jacobs, Eddie L.; Halford, Carl E.

doi:10.1109/jsen.2009.2038894

Cited by 17 publications

(13 citation statements)

References 4 publications

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“…The crude silhouettes generated from a sparse array of sensors would offer sufficient information for the classification task and the classification algorithms were tractable without complex image processing. Russomanno et al designed a sparse array of sensors with the near infrared (IR) transmitters and receivers for the border, perimeter and other intelligent electronic fence applications [27,28]. However, the proposed sensor belongs to an active version and it is difficult to deploy them in a nursing home.…”

Section: Related Workmentioning

confidence: 99%

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

Liu

2014

International Journal of Advanced Robotic Systems

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This article introduces a mobile infrared silhouette imaging and sparse representation-based pose recognition for building an elderly-fall detection system. The proposed imaging paradigm exploits the novel use of the pyroelectric infrared (PIR) sensor in pursuit of body silhouette imaging. A mobile robot carrying a vertical column of multi-PIR detectors is organized for the silhouette acquisition. Then we express the fall detection problem in silhouette image-based pose recognition. For the pose recognition, we use a robust sparse representation-based method for fall detection. The normal and fall poses are sparsely represented in the basis space spanned by the combinations of a pose training template and an error template. The 1 norm minimizations with linear programming (LP) and orthogonal matching pursuit (OMP) are used for finding the sparsest solution, and the entity with the largest amplitude encodes the class of the testing sample. The application of the proposed sensing paradigm to fall detection is addressed in the context of three scenarios, including: ideal non-obstruction, simulated random pixel obstruction and simulated random block obstruction. Experimental studies are conducted to validate the effectiveness of the proposed method for nursing and homeland healthcare.

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Section: Related Workmentioning

confidence: 99%

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

Liu

2014

International Journal of Advanced Robotic Systems

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“…Previous research indicated that 17 samples over a human, of height 2 m, provided sufficient information for accurate target discrimination. 9 The design process started with the choice of a Dias 128 element linear array of pyroelectric detectors. The size of each detector is 90 μm × 100 μm with a pitch of 100 μm.…”

Section: Sensor Specificationsmentioning

confidence: 99%

“…An example of a low resolution, low bit depth image includes an active near-infrared optical trip wire style system. [7][8][9] The system consists of two vertical posts with one post lined with near-infrared transmitters and receivers. The other post, placed directly opposite, consists of reflectors.…”

Section: Introductionmentioning

confidence: 99%

Pyroelectric linear array sensor for object recognition

Chari¹,

Jacobs

Choudhary

2014

Opt. Eng

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Abstract. This paper presents a proof of concept sensor system based on a linear array of pyroelectric detectors for recognition of moving objects. The utility of this prototype sensor is demonstrated by its use in trail monitoring and perimeter protection applications for classifying humans against animals with object motion transverse to the field of view of the sensor array. Data acquisition using the system was performed under varied terrains and using a wide variety of animals and humans. With the objective of eventually porting the algorithms onto a low resource computational platform, simple signal processing, feature extraction, and classification techniques are used. The object recognition algorithm uses a combination of geometrical and texture features to provide limited insensitivity to range and speed. Analysis of system performance shows its effectiveness in discriminating humans and animals with high classification accuracy.

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“…One application of profiling sensor technology that has received increased attention is intelligent fence applications [10], including security and surveillance of the United States (U.S.) and other areas. The U.S. has approximately 12,000 kilometers of border, with numerous isolated and unpopulated sections.…”

Section: Introductionmentioning

confidence: 99%

Advancing Profiling Sensors with a Wireless Approach

Galvis

Russomanno

2012

Sensors

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The notion of a profiling sensor was first realized by a Near-Infrared (N-IR) retro-reflective prototype consisting of a vertical column of wired sparse detectors. This paper extends that prior work and presents a wireless version of a profiling sensor as a collection of sensor nodes. The sensor incorporates wireless sensing elements, a distributed data collection and aggregation scheme, and an enhanced classification technique. In this novel approach, a base station pre-processes the data collected from the sensor nodes and performs data re-alignment. A back-propagation neural network was also developed for the wireless version of the N-IR profiling sensor that classifies objects into the broad categories of human, animal or vehicle with an accuracy of approximately 94%. These enhancements improve deployment options as compared with the first generation of wired profiling sensors, possibly increasing the application scenarios for such sensors, including intelligent fence applications.

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Near-IR Sparse Detector Sensor for Intelligent Electronic Fence Applications

Cited by 17 publications

References 4 publications

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

Mobile Robot Aided Silhouette Imaging and Robust Body Pose Recognition for Elderly-Fall Detection

Pyroelectric linear array sensor for object recognition

Advancing Profiling Sensors with a Wireless Approach

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