Human Tracking With Wireless Distributed Pyroelectric Sensors

Hao, Qi; Brady, David J.; Guenther, Bob D.; Burchett, John; Shankar, Mohan; Feller, Steven D.

doi:10.1109/jsen.2006.884562

Cited by 155 publications

(68 citation statements)

References 28 publications

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“…For reasons that will be apparent soon, we would like to make the PIR lenses small and thin and moldable from cheap plastic, even though it may add distortion [12]. For this reason the sensors are actually Fresnel lenses as shown in Fig 2. A person enters through main gate, IR sensors gets activated and the Microcontroller indicates the correct direction, the buzzer beeps and an led glows Indicating the entry is authorised [13] .In this case no operation of motor and laser gun is involved. But a person enters the premises apart from the main gate then PIR sensors get activated and they provide a positive output to 4049IC.…”

Section: Fig 1 Pir Motion Sensormentioning

confidence: 99%

Infra Red Radiation Detection using Passive Infrared Sensor

Abidin¹,

Ahuja²

2016

IJCA

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This paper proposes a new approach for infrared object localization and tracking with passive infrared sensors (PIR). The hierarchical architecture visibility of Fresnel lens away is presented with modulated field of view (FOV). The FOVs of lens array in sensor node are modulated to achieve a single degree of freedom (DOF). The energy imbalance problem effectively solve with the PIR system. PIR based system saves power consumption and memory space. Passive infrared system detects the change in the radiation of warm blood generation and completely used to turn On the webcam and lighting system.

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Section: Fig 1 Pir Motion Sensormentioning

confidence: 99%

Infra Red Radiation Detection using Passive Infrared Sensor

Abidin¹,

Ahuja²

2016

IJCA

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“…The PIR detector has several promising advantages: it is able to convert the incident thermal radiation into an electrical signal, and it responds to radiation with wavelengths ranging from 8µm to 14µm which just corresponds to the typical thermal radiation emitted from the human body [20]; the cost of a commercially available sensor is extremely low; the power consumption is low and suitable for wireless networks and mobile agent application. Because of these advantages, the PIR detector has been developed in lightweight biometric detection [21,22], human identification [23][24][25] and multiple human tracking [16,17].…”

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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“…Dr. Qi Hao et al first studied the feasibility of the use of PIR in motion detection. In [1], with the Field of View (FOV) of PIR sensor nodes partitioned into several sub-regions and encoded, the locaction of target is estimated through grid approximation. In [2], a target location estimation method is put forward using the angular bisectors of the FOV.…”

Section: Introductionmentioning

confidence: 99%

Bearing Locating Algorithm of Target based on Radial Basis Function Neural Network

Wang¹,

Tian²

2016

Proceedings of the 2nd Information Technology and Mechatronics Engineering Conference (ITOEC 2016TOEC 2016)

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Aiming at the high locating error and the underutilization of redundancy bearing measurement in Pyroelectric Infrared Sensor (PIR) location system, a novel method of bearing location based on Radial Basis Function Neural Network (RBFNN) is presented. After illustrating the region partition model of PIR sensor node, we take advantage of the K-means clustering method and the gradient-descent method to train the neural network. By comparing different sizes of training samples, we select a neural network model with lower locating error, and we have made a comparison of RBFNN and the geometric algorithm. The result of simulation shows that the neural network model has 18% higher locating accuracy and the locating error is much less than the geometric algorithm when the target is near the boundary of the detecting area.

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Human Tracking With Wireless Distributed Pyroelectric Sensors

Cited by 155 publications

References 28 publications

Infra Red Radiation Detection using Passive Infrared Sensor

Infra Red Radiation Detection using Passive Infrared Sensor

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

Bearing Locating Algorithm of Target based on Radial Basis Function Neural Network

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