Flow velocity tracking in thermal imagery
datacite.FundingReference.funderName | Europäische Union | |
Contributing person | Schütze, Niels (orcid: https://orcid.org/0000-0002-2376-528X) | |
Contributing person | Grundmann, Jens | |
Contributing person | Lin, Dong | |
Contributing person | Eltner, Anette | |
Description of the data | Lab_Experiments_Data file folder includes eight image sequences acquired in lab experiments. Note that these data are processed after radiometric calibration, which means that the vignetting effect existing in original data has already been removed and the image contrast has also been improved. These processed data are the inputs for PIVlab, PTVlab and LK tracking algorithms. Furthermore, inside of Lab_Experiments_Data file folder, the file folders of 1_8bit_ContrastImprovement, 2_8bit_ContrastImprovement and 3_8bit_ContrastImprovement correspond to the data of laboratory experiments 1-3. Similarly, the file folders of 4_8bit_ContrastImprovement, 5_8bit_ContrastImprovement and 6_8bit_ContrastImprovement correspond to the data of the laboratory experiments 4-6. the file folders of 7_8bit_ContrastImprovement, 8_8bit_ContrastImprovement and 9_8bit_ContrastImprovement correspond to the data of the laboratory experiments 7-9. While Field_Experiments_Data file folder includes two image sequences acquired in field experiments. Note that these data are processed after radiometric calibration, which means that the vignetting effect existing in original data has already been removed and the image contrast has also been improved. These processed data are the inputs for PIVlab, PTVlab and LK tracking algorithms. Furthermore, inside of Field_Experiments_Data file folder, the file folders of 1_8bit_ContrastImprovement and 2_8bit_ContrastImprovement correspond to the data of field experiments 1, 2 respectively. In addition, note that the frame rate of all of the image sequences is 30 HZ. | |
Type of the data | Image | |
Total size of the dataset | 235025376 | |
Author | Lin, Dong | |
Upload date | 2018-11-16T12:19:08Z | |
Publication date | 2018-11-16T12:19:08Z | |
Publication date | 2026-05-18T12:41:51Z | |
Data of data creation | 2018 | |
Publication date | 2018-11-16 | |
Abstract of the dataset | Image data set to the submitted WRR publication ”Evaluating Image Tracking Approaches for Surface Velocimetry with Thermal Tracers“. In this paper an automatic approach is proposed to measure flow velocity with an uncooled thermal camera. Hot water is used as thermal tracer. The introduced tracking algorithm utilizes the pyramidal Lucas-Kanade method and is especially suitable for thermal image data. The performance of the new tool is compared to traditional image-based tracking tools, i.e. PIVlab and PTVlab. Experiments are performed in the laboratory for three different flow velocities. Afterwards, tests are conducted in a small stream to illustrate the suitability of the tool for field measurements. Results of the laboratory experiments as well as of the field experiments show that our tracking algorithm, applied to imagery from a thermal camera, outperforms commonly used tracking methods. Our tool provides velocity fields with very high resolution and is in close agreement with reference measurements, whereas PTVlab and PIVlab tend to overestimate and underestimate flow velocities, respectively. | |
Public reference to this page | https://opara.zih.tu-dresden.de/handle/123456789/2310 | |
Public reference to this page | https://doi.org/10.25532/OPARA-21 | |
dc.language | eng | |
Publisher | Technische Universität Dresden | |
Licence | Attribution-NonCommercial 4.0 International | |
URI of the licence text | http://creativecommons.org/licenses/by-nc/4.0/ | |
Specification of the discipline(s) | 3::34 | |
Title of the dataset | Flow velocity tracking in thermal imagery | |
Project abstract | The project goal is the development of innovative techniques for spatio-temporal high resolution monitoring and small-scale simulation of extreme events. In a cooperation between the chairs of Hydrology, Meteorology, Geoinformatics and Photogrammetry, of the TU Dresden, new types of operational monitoring systems will be developed. Existing monitoring networks will be densified using modern low-cost sensors, specific remote sensing data and geographical information systems. Additionally, historical analyses and predictive modelling of small-scale extreme events with different climate scenarios will support to predict the expected effects of climate change. The developed information will serve as a base for upcoming early warning systems and future adjustment strategies. | |
Project title | EXTRUSO - Extreme Events in Small and Medium Scale Catchments (EXTRUSO) |
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