Geometric calibration of thermal cameras

2013 ◽  
Author(s):  
Philip Engström ◽  
Håkan Larsson ◽  
Joakim Rydell
Keyword(s):  
Geometric Calibration ◽  
Thermal Cameras

Geometric Calibration of Thermal Cameras

2011 ◽  
Vol 2011 (1) ◽  
pp. 5-15 ◽  
Author(s):  
Thomas Luhmann ◽  
Julia Ohm ◽  
Johannes Piechel ◽  
Thorsten Roelfs
Keyword(s):  
Geometric Calibration ◽  
Thermal Cameras

scholarly journals JOINT GEOMETRIC CALIBRATION OF COLOR AND THERMAL CAMERAS FOR SYNCHRONIZED MULTIMODAL DATASET CREATING

2019 ◽  
Vol XLII-2/W18 ◽  
pp. 79-84 ◽  
Author(s):  
V. A. Knyaz ◽  
P. V. Moshkantsev
Keyword(s):  
Computer Vision ◽  
Parameters Estimation ◽  
Industrial Control ◽  
Geometric Calibration ◽  
Long Wave ◽  
Infra Red ◽  
Thermal Cameras ◽  
Thermal Vision ◽  
Model Training ◽  
And Robotics

Abstract. With increasing performance and availability of thermal cameras the number of applications using them in various purposes grows noticeable. Nowadays thermal vision is widely used in industrial control and monitoring, thermal mapping of industrial areas, surveillance and robotics which output huge amount of thermal images. This circumstance creates the necessary basis for applying deep learning which demonstrates the state-of-the-art performance for the most complicated computer vision tasks. Using different modalities for scene analysis allows to outperform results of mono-modal processing, but in case of machine learning it requires synchronized annotated multimodal dataset. The prerequisite condition for such dataset creating is geometric calibration of sensors used for image acquisition. So the purpose of the performed study was to develop a technique for joint calibration of color and long wave infra-red cameras which are to be used for collecting multimodal dataset needed for the tasks of computer vision algorithms developing and evaluating.The paper presents the techniques for camera parameters estimation and experimental evaluation of interior orientation of color and long wave infra-red cameras for further exploiting in datasets collecting. Also the results of geometrically calibrated camera exploiting for 3D reconstruction and 3D model realistic texturing based on visible and thermal imagery are presented. They proved the effectivity of the developed techniques for collecting and augmenting synchronized multimodal imagery dataset for convolutional neural networks model training and evaluating.

scholarly journals Performance evaluation of thermographic cameras for photogrammetric documentation of historical buildings

2013 ◽  
Vol 19 (4) ◽  
pp. 711-728 ◽  
Author(s):  
Naci Yastikli ◽  
Esra Guler
Keyword(s):  
Performance Evaluation ◽  
Focal Length ◽  
Digital Camera ◽  
Infrared Region ◽  
Historical Buildings ◽  
Principal Point ◽  
Geometric Calibration ◽  
Thermal Cameras ◽  
Thermographic Camera ◽  
And Performance

Thermographic cameras record temperatures emitted by objects in the infrared region. These thermal images can be used for texture analysis and deformation caused by moisture and isolation problems. For accurate geometric survey of the deformations, the geometric calibration and performance evaluation of the thermographic camera should be conducted properly. In this study, an approach is proposed for the geometric calibration of the thermal cameras for the geometric survey of deformation caused by moisture. A 3D test object was designed and used for the geometric calibration and performance evaluation. The geometric calibration parameters, including focal length, position of principal point, and radial and tangential distortions, were determined for both the thermographic and the digital camera. The digital image rectification performance of the thermographic camera was tested for photogrammetric documentation of deformation caused by moisture. The obtained results from the thermographic camera were compared with the results from digital camera based on the experimental investigation performed on a study area.

Cross-Calibration of RGB and Thermal Cameras with a LIDAR for RGB-Depth-Thermal Mapping

2017 ◽  
Vol 05 (02) ◽  
pp. 59-78 ◽  
Author(s):  
Aravindhan K Krishnan ◽  
Srikanth Saripalli
Keyword(s):  
Objective Function ◽  
Calibration Procedure ◽  
Checkerboard Pattern ◽  
Thermal Camera ◽  
Calibration Target ◽  
Extrinsic Calibration ◽  
Geometric Calibration ◽  
Extrinsic Parameters ◽  
Thermal Cameras ◽  
Cross Calibration

We present a method for calibrating the extrinsic parameters between a RGB camera, a thermal camera, and a LIDAR. The calibration procedure we use is common to both the RGB and thermal cameras. The extrinsic calibration procedure assumes that the cameras are geometrically calibrated. To aid the geometric calibration of the thermal camera, we use a calibration target made of black-and-white melamine that looks like a checkerboard pattern in the thermal and RGB images. For the extrinsic calibration, we place a circular calibration target in the common field of view of the cameras and the LIDAR and compute the extrinsic parameters by minimizing an objective function that aligns the edges of the circular target in the LIDAR to its corresponding edges in the RGB and thermal images. We illustrate the convexity of the objective function and discuss the convergence of the algorithm. We then identify the various sources of coloring errors (after cross-calibration) as (a) noise in the LIDAR points, (b) error in the intrinsic parameters of the camera, (c) error in the translation parameters between the LIDAR and the camera and (d) error in the rotation parameters between the LIDAR and the camera. We analyze the contribution of these errors with respect to the coloring of a 3D point. We illustrate that these errors are related to the depth of the 3D point considered — with errors (a), (b), and (c) being inversely proportional to the depth, and error (d) being directly proportional to the depth.

scholarly journals Towards human distance estimation using a thermal sensor array

2021 ◽  
Author(s):  
Abdallah Naser ◽  
Ahmad Lotfi ◽  
Joni Zhong
Keyword(s):  
High Resolution ◽  
Independent Living ◽  
Sensor Array ◽  
Distance Estimation ◽  
Thermal Sensor ◽  
Indoor Environments ◽  
Contagious Diseases ◽  
Novel Approach ◽  
Thermal Cameras ◽  
Distance Estimator

AbstractHuman distance estimation is essential in many vital applications, specifically, in human localisation-based systems, such as independent living for older adults applications, and making places safe through preventing the transmission of contagious diseases through social distancing alert systems. Previous approaches to estimate the distance between a reference sensing device and human subject relied on visual or high-resolution thermal cameras. However, regular visual cameras have serious concerns about people’s privacy in indoor environments, and high-resolution thermal cameras are costly. This paper proposes a novel approach to estimate the distance for indoor human-centred applications using a low-resolution thermal sensor array. The proposed system presents a discrete and adaptive sensor placement continuous distance estimators using classification techniques and artificial neural network, respectively. It also proposes a real-time distance-based field of view classification through a novel image-based feature. Besides, the paper proposes a transfer application to the proposed continuous distance estimator to measure human height. The proposed approach is evaluated in different indoor environments, sensor placements with different participants. This paper shows a median overall error of $$\pm 0.2$$ ± 0.2  m in continuous-based estimation and $$96.8\%$$ 96.8 % achieved-accuracy in discrete distance estimation.

scholarly journals Monitoring of Khorasan (Triticum turgidum ssp. turanicum) and Modern Kabot Spring Wheat (Triticum aestivum) Varieties by UAV and Sensor Technologies under Different Soil Tillage

Agronomy ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1348
Author(s):  
Kristýna Balážová ◽  
Jan Chyba ◽  
Jitka Kumhálová ◽  
Jiří Mašek ◽  
Stanislav Petrásek
Keyword(s):  
Triticum Aestivum ◽  
Spring Wheat ◽  
Triticum Turgidum ◽  
Soil Analysis ◽  
Low Cost ◽  
Wheat Variety ◽  
Soil Surface ◽  
Conventional Tillage ◽  
Soil Tillage ◽  
Thermal Cameras

Khorasan wheat (Triticum turgidum ssp. turanicum (Jakubz.)) is an ancient tetraploid spring wheat variety originating from northeast parts of Central Asia. This variety can serve as a full-fledged alternative to modern wheat but has a lower yield than modern varieties. It is commonly known that wheat growth is influenced by soil tillage technology (among other things). However, it is not known how soil tillage technology affects ancient varieties. Therefore, the main objective of this study was to evaluate the influence of different soil tillage technologies on the growth of the ancient Khorasan wheat variety in comparison to the modern Kabot spring wheat (Triticum aestivum) variety. The trial was arranged in six small plots, one half of which was sown by the Khorasan wheat variety and the other half of which was sown by the Kabot wheat variety. Three soil tillage methods were used for each cultivar: conventional tillage (CT) (20–25 cm), minimum tillage (MTC) with a coulter cultivator (15 cm), and minimization tillage (MTD) with a disc cultivator (12 cm). The soil surface of all of the variants were leveled after tillage (harrows & levelling bars). An unmanned aerial vehicle with multispectral and thermal cameras was used to monitor growth during the vegetation season. The flight missions were supplemented by measurements using the GreenSeeker hand-held sensor and plant and soil analysis. The results showed that the Khorasan ancient wheat was better suited the conditions of conventional tillage, with low values of bulk density and highvalues of total soil porosity, which generally increased the nutritional value of the yield in this experimental plot. At the same time, it was found that this ancient wheat does not deplete the soil. The results also showed that the trend of developmental growing curves derived from different sensors was very similar regardless of measurement method. The sensors used in this study can be good indicators of micronutrient content in the plant as well as in the grains. A low-cost RGB camera can provide relevant results, especially in cases where equipment that is more accurate is not available.

scholarly journals Water-Stressed Plants Do Not Cool: Leaf Surface Temperature of Living Wall Plants under Drought Stress

2021 ◽  
Vol 13 (7) ◽  
pp. 3910
Author(s):  
Michael Gräf ◽  
Markus Immitzer ◽  
Peter Hietz ◽  
Rosemarie Stangl
Keyword(s):  
Drought Stress ◽  
Surface Temperature ◽  
Leaf Surface ◽  
Canopy Temperature ◽  
Infrared Camera ◽  
Control Group ◽  
Thermal Cameras ◽  
Living Wall ◽  
Leaf Level ◽  
Vertical Greening

Urban green infrastructures offer thermal regulation to mitigate urban heat island effects. To gain a better understanding of the cooling ability of transpiring plants at the leaf level, we developed a method to measure the time series of thermal data with a miniaturized, uncalibrated thermal infrared camera. We examined the canopy temperature of four characteristic living wall plants (Heuchera x cultorum, Bergenia cordifolia, Geranium sanguineum, and Brunnera macrophylla) under increasing drought stress and compared them with a well-watered control group. The method proved suitable to evaluate differences in canopy temperature between the different treatments. Leaf temperatures of water-stressed plants were 6 to 8 °C higher than those well-watered, with differences among species. In order to cool through transpiration, vegetation in green infrastructures must be sufficiently supplied with water. Thermal cameras were found to be useful to monitor vertical greening because leaf surface temperature is closely related to drought stress. The usage of thermal cameras mounted on unmanned aerial vehicles could be a rapid and easy monitoring system to cover large façades.

scholarly journals A New Combined Adjustment Model for Geolocation Accuracy Improvement of Multiple Sources Optical and SAR Imagery

2021 ◽  
Vol 13 (3) ◽  
pp. 491
Author(s):  
Niangang Jiao ◽  
Feng Wang ◽  
Hongjian You
Keyword(s):  
Remote Sensing Data ◽  
Observation Data ◽  
Multiple Sources ◽  
Accuracy Improvement ◽  
Geometric Calibration ◽  
Proposed Model ◽  
Calibration Methods ◽  
Earth Observation Data ◽  
Sar Imagery ◽  
Geolocation Accuracy

Numerous earth observation data obtained from different platforms have been widely used in various fields, and geometric calibration is a fundamental step for these applications. Traditional calibration methods are developed based on the rational function model (RFM), which is produced by image vendors as a substitution of the rigorous sensor model (RSM). Generally, the fitting accuracy of the RFM is much higher than 1 pixel, whereas the result decreases to several pixels in mountainous areas, especially for Synthetic Aperture Radar (SAR) imagery. Therefore, this paper proposes a new combined adjustment for geolocation accuracy improvement of multiple sources satellite SAR and optical imagery. Tie points are extracted based on a robust image matching algorithm, and relationships between the parameters of the range-doppler (RD) model and the RFM are developed by transformed into the same Geodetic Coordinate systems. At the same time, a heterogeneous weight strategy is designed for better convergence. Experimental results indicate that our proposed model can achieve much higher geolocation accuracy with approximately 2.60 pixels in the X direction and 3.50 pixels in the Y direction. Compared with traditional methods developed based on RFM, our proposed model provides a new way for synergistic use of multiple sources remote sensing data.

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