Automatic water-level detection using single-camera images with varied poses

Lin, Yan-Ting; Lin, Yi-Chun; Han, Jen-Yu

doi:10.1016/j.measurement.2018.05.100

Cited by 59 publications

(52 citation statements)

References 15 publications

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“…Otherwise, image drifts can lead to false measurements of the water level, for instance considering the case that the water level increases but an image shift towards the riverbed would compensate the trend. In case of flood observation, the errors resulting from temperature-related changes in the IOP can be neglected because the reliability of water level estimation, e.g., with methods introduced by e.g., [4,43,44], decreases strongly due to large waves at the water surface that impede a unique detection of the shore line.…”

Section: Resultsmentioning

confidence: 99%

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Elias

Eltner

Liebold

et al. 2020

Sensors

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Knowledge about the interior and exterior camera orientation parameters is required to establish the relationship between 2D image content and 3D object data. Camera calibration is used to determine the interior orientation parameters, which are valid as long as the camera remains stable. However, information about the temporal stability of low-cost cameras due to the physical impact of temperature changes, such as those in smartphones, is still missing. This study investigates on the one hand the influence of heat dissipating smartphone components at the geometric integrity of implemented cameras and on the other hand the impact of ambient temperature changes at the geometry of uncoupled low-cost cameras considering a Raspberry Pi camera module that is exposed to controlled thermal radiation changes. If these impacts are neglected, transferring image measurements into object space will lead to wrong measurements due to high correlations between temperature and camera’s geometric stability. Monte-Carlo simulation is used to simulate temperature-related variations of the interior orientation parameters to assess the extent of potential errors in the 3D data ranging from a few millimetres up to five centimetres on a target in X- and Y-direction. The target is positioned at a distance of 10 m to the camera and the Z-axis is aligned with camera’s depth direction.

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Section: Resultsmentioning

confidence: 99%

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Elias

Eltner

Liebold

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Object-scale computing using the photogrammetric method can be facilitated by introducing control points to link an image to world coordinates [35]. Previous studies employed image information, including camera-known parameters and water gauges, to develop water level monitoring systems [36][37][38]. The interior orientation includes the focal length, the location of the principal point, and the description of lens distortion.…”

Section: Establishing Coordinate Systems Using Image-based Vgimentioning

confidence: 99%

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

et al. 2020

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Many people use smartphone cameras to record their living environments through captured images, and share aspects of their daily lives on social networks, such as Facebook, Instagram, and Twitter. These platforms provide volunteered geographic information (VGI), which enables the public to know where and when events occur. At the same time, image-based VGI can also indicate environmental changes and disaster conditions, such as flooding ranges and relative water levels. However, little image-based VGI has been applied for the quantification of flooding water levels because of the difficulty of identifying water lines in image-based VGI and linking them to detailed terrain models. In this study, flood detection has been achieved through image-based VGI obtained by smartphone cameras. Digital image processing and a photogrammetric method were presented to determine the water levels. In digital image processing, the random forest classification was applied to simplify ambient complexity and highlight certain aspects of flooding regions, and the HT-Canny method was used to detect the flooding line of the classified image-based VGI. Through the photogrammetric method and a fine-resolution digital elevation model based on the unmanned aerial vehicle mapping technique, the detected flooding lines were employed to determine water levels. Based on the results of image-based VGI experiments, the proposed approach identified water levels during an urban flood event in Taipei City for demonstration. Notably, classified images were produced using random forest supervised classification for a total of three classes with an average overall accuracy of 88.05%. The quantified water levels with a resolution of centimeters (<3-cm difference on average) can validate flood modeling so as to extend point-basis observations to area-basis estimations. Therefore, the limited performance of image-based VGI quantification has been improved to help in flood disasters. Consequently, the proposed approach using VGI images provides a reliable and effective flood-monitoring technique for disaster management authorities.

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“…In order to obtain the subsidence automatically, it is necessary to design the measuring mark that both provides necessary mathematical information and is easily recognized. The measuring mark must be readable so that the results can be verified manually [22]. The common settlement dot marks are designed for a levelling rod, which cannot be used for photogrammetry.…”

Section: Subsidence Mark Designmentioning

confidence: 99%

Research on the Design and Automatic Recognition Algorithm of Subsidence Marks for Close-Range Photogrammetry

Zou

Guo-jian

2020

Sensors

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In China, traditional techniques for measuring structural subsidence cannot keep pace with the rapid development of critical national infrastructure such as the growing network of high-speed railways. Traditional monitoring methods using leveling instruments are inefficient and time consuming when monitoring structures like bridges and tunnels. Thus, a fast, economical, and more accurate and precise way to survey building subsidence is urgently needed to address this problem. This paper introduces a new close-range photogrammetry technique that deploys a fixed camera with tilt compensator to measure changes in height over small areas. A barcode subsidence mark that can be identified automatically during digital image processing replaces the leveling points used in traditional methods. Four experiments at different locations verified that results from the new method were stable and consistent with total station measurements. This approach is simple, inexpensive, and produces accurate and precise results as our evaluation results show.

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Automatic water-level detection using single-camera images with varied poses

Cited by 59 publications

References 15 publications

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Assessing the Influence of Temperature Changes on the Geometric Stability of Smartphone- and Raspberry Pi Cameras

Quantifying Flood Water Levels Using Image-Based Volunteered Geographic Information

Research on the Design and Automatic Recognition Algorithm of Subsidence Marks for Close-Range Photogrammetry

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