Real-time assessment of a linear pyroelectric sensor array for object classication

White, William E.; Brown, Jeremy B.; Chari, Srikant; Jacobs, Eddie L.

doi:10.1117/12.865168

Cited by 5 publications

(8 citation statements)

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“…Initial design and testing of a linear array using such a system showed promising results. 12 Another aspect that differentiates the research reported here from the previous efforts is that, in this paper, a two class problems of discriminating humans versus animals is addressed. Further, a more comprehensive performance analysis is performed using a larger data base of objects.…”

Section: Introductionmentioning

confidence: 82%

“…24 In this case, each model under evaluation is an FMM with a particular value of K and the model with the lowest AIC value is chosen. AIC is given by AIC ¼ −2 ln½PðxjθÞ þ 2Q; (12) whereθ are the estimated parameters of the model and Q is the number of parameters of the statistical model. The AIC equation is composed of two parts, the negative log-likelihood that measures how well the model under test fits the data and the number of parameters which penalizes a model for over fitting.…”

Section: Gaussian Mixture Modelmentioning

confidence: 99%

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Pyroelectric linear array sensor for object recognition

Chari¹,

Jacobs

Choudhary

2014

Opt. Eng

Self Cite

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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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Section: Introductionmentioning

confidence: 82%

Section: Gaussian Mixture Modelmentioning

confidence: 99%

Pyroelectric linear array sensor for object recognition

Chari¹,

Jacobs

Choudhary

2014

Opt. Eng

Self Cite

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“…The transmitters were mounted on the first pole and create a N-IR beam that is perpendicular to the paired reflecting surface mounted on the opposite pole (Figure 1(a)). In this configuration, the vertical profiling sensor has been shown to successfully generate and accurately classify silhouettes among predefined classification groups [1–5]. For this initial prototype, there was a zero horizontal offset between the transmitters and receivers.…”

Section: Profiling Sensors That Use a Sparse Detector Arraymentioning

confidence: 99%

“…A vertical detector array, consisting of sixteen pairs of transmitters and reflectors, was first developed to produce sixteen parallel N-IR beams. A silhouette was then generated if the object broke the sensor’s beams [1–5]. …”

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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“…Jacobs et al introduced the concept of passive PIR sensor-based silhouette imaging and gave a simulation based on thermal infrared video sequences [29]. Willianm et al presented a pyroelectric linear array-based silhouette sensor for distinguishing humans from animals, but they did not discuss the application of the analysis of human poses [30]. It should be noted that the field of view (FOV) of the above-mentioned solutions is fixed and it difficult to extend using sensor networks or mobile agents for home healthcare situations.…”

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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Real-time assessment of a linear pyroelectric sensor array for object classication

Cited by 5 publications

References 0 publications

Pyroelectric linear array sensor for object recognition

Pyroelectric linear array sensor for object recognition

Advancing Profiling Sensors with a Wireless Approach

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

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