scholarly journals Indoor Human Detection Based on Thermal Array Sensor Data and Adaptive Background Estimation

2017 ◽  
Vol 05 (04) ◽  
pp. 16-28 ◽  
Author(s):  
Anna A. Trofimova ◽  
Andrea Masciadri ◽  
Fabio Veronese ◽  
Fabio Salice
Keyword(s):  
Human Detection ◽  
Sensor Data ◽  
Background Estimation ◽  
Array Sensor

scholarly journals Low Power AFE Circuit Supporting IR Array Sensor for Human Detection

2016 ◽  
Vol 20 (4) ◽  
pp. 133-136
Author(s):  
Shota Ueguchi ◽  
Mitsuhiro Nagao ◽  
Toshio Kumamoto ◽  
Masayoshi Shirahata ◽  
Takeshi Kumaki ◽  
...  
Keyword(s):  
Low Power ◽  
Human Detection ◽  
Array Sensor

scholarly journals Monitoring Indoor People Presence in Buildings Using Low-Cost Infrared Sensor Array in Doorways

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4062
Author(s):  
Cristian Perra ◽  
Amit Kumar ◽  
Michele Losito ◽  
Paolo Pirino ◽  
Milad Moradpour ◽  
...  
Keyword(s):  
Low Cost ◽  
Synthetic Data ◽  
Field Evaluation ◽  
Sensor Data ◽  
Indoor Environments ◽  
Stress Tests ◽  
Ambient Conditions ◽  
Time Pattern ◽  
Array Sensor ◽  
Depth Analysis

We propose a device for monitoring the number of people who are physically present inside indoor environments. The device performs local processing of infrared array sensor data detecting people’s direction, which allows monitoring users’ occupancy in any space of the building and also respects people privacy. The device implements a novel real-time pattern recognition algorithm for processing data sensed by a low-cost infrared (IR) array sensor. The computed information is transferred through a Z-Wave network. On-field evaluation of the algorithm has been conducted by placing the device on top of doorways in offices and laboratory rooms. To evaluate the performance of the algorithm in varying ambient temperatures, two groups of stress tests have been designed and performed. These tests established the detection limits linked to the difference between the average ambient temperature and perturbation. For an in-depth analysis of the accuracy of the algorithm, synthetic data have been generated considering temperature ranges typical of a residential environment, different human walking speeds (normal, brisk, running), and distance between the person and the sensor (1.5 m, 5 m, 7.5 m). The algorithm performed with high accuracy for routine human passage detection through a doorway, considering indoor ambient conditions of 21–30 °C.

scholarly journals TOWARDS DETECTION OF THERMAL ANOMALIES IN LARGE URBAN AREAS USING SIMULATION

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 1195-1202
Author(s):  
E. Burkard ◽  
D. Bulatov ◽  
B. Kottler
Keyword(s):  
Anomaly Detection ◽  
Urban Areas ◽  
Infrared Image ◽  
Thermal Image ◽  
Sensor Data ◽  
Infrared Images ◽  
Thermal Anomalies ◽  
Image Differencing ◽  
Background Estimation ◽  
Elevation Data

Abstract. Anomaly detection in imagery has widely been studied and enhanced towards the requirements of today’s available sensor data, whereas many of them require a background estimation in order to identify an anomaly or target. In this paper, we examine an analysis of simulation as background estimator for anomaly detection in thermal images of urban sceneries. We generate a surface temperature image and a sensor-like infrared image by combined image and elevation data and a thermal model suited for large scenes and fast simulation. With the simulated thermal image, we define anomalies as deviation between measurement and simulation. Pixel-wise image differencing of the measured and simulated temperatures and infrared images respectively are performed and evaluated concerning the full images as well as class-wise, including a material classification of the observed area. Our approach shows complementary results compared to RXD application on the measured infrared images. Metal roofs which appear warm in the thermal image and are not visually distinguishable from the residual image are detected.

Adapting physically complete models to vehicle-based EMI array sensor data: data inversion and discrimination studies

2010 ◽  
Author(s):  
Fridon Shubitidze ◽  
Jonathan S. Miller ◽  
Gregory M. Schultz ◽  
Jay A. Marble
Keyword(s):  
Sensor Data ◽  
Array Sensor ◽  
Data Inversion
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