scholarly journals Sensitivity enhancement of bimaterial MOEMS thermal imaging sensor array using 2-λ wavelength readout

2010 ◽  
Author(s):  
O. Ferhanoĝlu ◽  
H. Urey
Keyword(s):  
Thermal Imaging ◽  
Sensor Array ◽  
Sensitivity Enhancement ◽  
Imaging Sensor

A Thermal Imaging Sensor Evaluation Facility.

1977 ◽  
Author(s):  
Donald V. Campbell ◽  
Johnson Jr. ◽  
Mallory Jeffrey P. ◽  
William R.
Keyword(s):  
Thermal Imaging ◽  
Imaging Sensor

scholarly journals Target-Tracking based on Fusion of Unsynchronized Sensor Data from Vision System and Thermal Imaging Sensor

2012 ◽  
Vol 25 (11) ◽  
pp. 323-327 ◽  
Author(s):  
Tomohiro Umetani ◽  
Naomichi Kuga ◽  
Masahiro Tanaka ◽  
Masahiro Wada ◽  
Minoru Ito
Keyword(s):  
Target Tracking ◽  
Thermal Imaging ◽  
Vision System ◽  
Sensor Data ◽  
Imaging Sensor

An 80x80 microbolometer type thermal imaging sensor using the LWIR-band CMOS infrared (CIR) technology

2017 ◽  
Author(s):  
Firat Tankut ◽  
Mustafa H. Cologlu ◽  
Hidir Askar ◽  
Hande Ozturk ◽  
Hilal K. Dumanli ◽  
...  
Keyword(s):  
Thermal Imaging ◽  
Imaging Sensor

scholarly journals Low-Cost Thermal Camera-Based Counting Occupancy Meter Facilitating Energy Saving in Smart Buildings

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4542
Author(s):  
Marek Kraft ◽  
Przemysław Aszkowski ◽  
Dominik Pieczyński ◽  
Michał Fularz
Keyword(s):  
Thermal Imaging ◽  
Network Architecture ◽  
Low Cost ◽  
Machine Learning Algorithms ◽  
Infrared Sensors ◽  
Neural Network Architecture ◽  
Binary Information ◽  
Reduce Energy Consumption ◽  
Imaging Sensor ◽  
Established Technique

Using passive infrared sensors is a well-established technique of presence monitoring. While it can significantly reduce energy consumption, more savings can be made when utilising more modern sensor solutions coupled with machine learning algorithms. This paper proposes an improved method of presence monitoring, which can accurately derive the number of people in the area supervised with a low-cost and low-energy thermal imaging sensor. The method utilises U-Net-like convolutional neural network architecture and has a low parameter count, and therefore can be used in embedded scenarios. Instead of providing simple, binary information, it learns to estimate the occupancy density function with the person count and approximate location, allowing the system to become considerably more flexible. The tests show that the method compares favourably to the state of the art solutions, achieving significantly better results.

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