A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras

Wang, Xing; Wang, Meizhen; Liu, Xuejun; Zhu, Liang; Glade, Thomas; Chen, Mingzheng; Wei, Zhao; Xie, Yujia

doi:10.1016/j.jhydrol.2021.127312

Cited by 9 publications

(10 citation statements)

References 39 publications

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“…An ensemble of surface velocimetry techniques could be applied, given sufficient computational power, to provide an additional quantification of algorithm-based uncertainty similar to ensemble approaches employed in other fields for quantifying model structural sensitivity (Nearing and Gupta, 2018), and facilitate the identification of disagreements between methods under particular sites and conditions over time while expanding the broader applicability of the technology through the advantages offered by each technique. Furthermore, the optical nature of the methods developed supposes the possibility for the incorporation of additional computer vision analysis through rainfall (Jiang et al, 2019;Chen et al, 2019;Wang et al, 2022), wind (Cardona, 2021), and water turbidity (Leeuw and Boss, 2018) monitoring.…”

Section: Discussionmentioning

confidence: 99%

Adaptively monitoring streamflow using a stereo computer vision system

Hutley

Beecroft²,

Wagenaar³

et al. 2022

Preprint

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Abstract. The gauging of free surface flows in waterways provides the foundation for monitoring and managing the water resources of built and natural environments. A significant body of literature exists around the techniques and benefits of optical surface velocimetry methods to estimate flows in waterways without intrusive instruments or structures. However, to date the operational application of these surface velocimetry methods has been limited by site configuration and inherent challenging optical variability across different natural and constructed waterway environments. This work demonstrates a significant advancement in the operationalisation of non-contact stream discharge gauging applied in the computer vision stream gauging (CVSG) system through the use of methods for remotely estimating water levels and adaptively learning discharge ratings over time. A cost-effective stereo camera-based stream gauging device (CVSG device) has been developed for streamlined site deployments and automated data collection. Evaluations between reference state-of-the-art discharge measurement technologies using DischargeLab (using surface structure image velocimetry), Hydro-STIV (using space-time image velocimetry), ADCPs (acoustic doppler current profilers), and gauging station discharge ratings demonstrated that the optical surface velocimetry methods were capable of estimating discharge within best available measurement error margins of 5–15 %. Furthermore, results indicated model machine learning approaches leveraging data to improve performance over a period of months at the study sites produced a marked 5–10 % improvement in discharge estimates, despite underlying noise in stereophotogrammetry water level or optical flow measurements. The operationalisation of optical surface velocimetry technology, such as CVSG, offers substantial advantages towards not only improving the overall density and availability of data used in stream gauging, but also providing a safe and non-contact approach for effectively measuring high flow rates while providing an adaptive solution for gauging streams with non-stationary characteristics.

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Section: Discussionmentioning

confidence: 99%

Adaptively monitoring streamflow using a stereo computer vision system

Hutley

Beecroft²,

Wagenaar³

et al. 2022

Preprint

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“…Thus, geotagged video is an important source of geographic information that has received attention in many fields, such as GIS, computer vision and data mining (Luo et al, 2011). Several studies have been performed in this area over the last several years, including on geotagged video data collection and processing (Burr et al, 2018; Mills et al, 2010); data modeling (Han et al, 2016; Lewis et al, 2011); video retrieval based on spatial semantics (Kim et al, 2010; Lu & Shahabi, 2017; Ma et al, 2014; Wu et al, 2018); video mapping, analysis, and mining (Jamonnak et al, 2021; Rumora et al, 2021; Wang et al, 2022; Zhang et al, 2021); and video synopsis (Jamonnak et al, 2020; Xie et al, 2022; Zhang et al, 2019). The integration and fusion of surveillance video and GIS can significantly improve the management efficiency of surveillance video and enhance video surveillance systems.…”

Section: Related Studiesmentioning

confidence: 99%

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

Liu

Han

Song

et al. 2023

Transactions in GIS

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Increasing concern for urban public safety has motivated the deployment of a large number of surveillance cameras in open spaces such as city squares, stations, and shopping malls. The efficient detection of crowd dynamics in urban open spaces using multi‐viewpoint surveillance videos continues to be a fundamental problem in the field of urban security. The use of existing methods for extracting features from video images has resulted in significant progress in single‐camera image space. However, surveillance videos are geotagged videos with location information, and few studies have fully exploited the spatial semantics of these videos. In this study, multi‐viewpoint videos in geographic space are used to fuse object trajectories for crowd sensing and spatiotemporal analysis. The YOLOv3‐DeepSORT model is used to detect a pedestrian and extract the corresponding image coordinates, combine spatial semantics (such as the positions of the pedestrian in the field of view of the camera) to build a projection transformation matrix and map the object recorded by a single camera to geographic space. Trajectories from multi‐viewpoint videos are fused based on the features of location, time, and directions to generate a complete pedestrian trajectory. Then, crowd spatial pattern analysis, density estimation, and motion trend analysis are performed. Experimental results demonstrate that the proposed method can be used to identify crowd dynamics and analyze the corresponding spatiotemporal pattern in an urban open space from a global perspective, providing a means of intelligent spatiotemporal analysis of geotagged videos.

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“…Schematic diagram of raindrop imaging based on camera optical principles based on figures from Jiang et al (2019) and Wang, Wang, Liu, Zhu, et al (2022). The rain‐streak image and real raindrop motion are shown, and their conversion relationships are shown in Equations (4) and (5).…”

Section: Modeling Process and Main Principlesmentioning

confidence: 99%

“…The theoretical sampling volume is calculated with the near‐focus and far‐focus lengths. Wang, Wang, Liu, Zhu, et al (2022) discussed in detail the various situations in which the conical range of the DoF intersects with the surface under different camera heights, pitch angles, and focal lengths and refined the calculation of the sampling volume. Aside from camera parameters, the calculation of sampling volume is affected by both the monitoring scene and rainfall intensity.…”

Section: Challenges In Video‐based Rainfall Monitoringmentioning

confidence: 99%

A review of video‐based rainfall measurement methods

Yan

Chen

et al. 2023

WIREs Water

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Accurate and high spatiotemporal resolution rainfall observations are essential for hydrological forecasting and flood management, especially in urban hydrological applications. However, it is difficult for traditional rainfall gauges, weather radars, and satellites to accurately estimate rainfall while simultaneously capturing the spatial and temporal variability of rainfall well. In this context, video‐based rainfall measurement, a novel method, has the advantages of real‐time performance and low cost and may thus provide a new way to establish rainfall observation networks with high spatial and temporal resolution. In recent years, different algorithms have been developed to recognize raindrops and estimate rainfall from rainfall videos. It has been demonstrated that video‐based rainfall measurement methods can provide comprehensive rainfall information with fine spatial and temporal granularity. However, raindrop visibility and the depth of field effects are difficult to address. The motion blur effect of raindrops may result in substantial errors and uncertainties. A fundamental problem of video‐based rainfall measurements lies in locating raindrops and accurately calculating their actual size. Moreover, the effectiveness of deep learning‐based video rainfall measurement models is greatly influenced by the diversity of the training data. Therefore, enhancing the high robustness and accuracy of video‐based rainfall measurement algorithms and increasing the computational efficiency are paramount to further development, which are prerequisites for their application in practical rainfall monitoring and developing multicamera monitoring networks.This article is categorized under: Science of Water > Methods Science of Water > Hydrological Processes

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A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras

Cited by 9 publications

References 39 publications

Adaptively monitoring streamflow using a stereo computer vision system

Adaptively monitoring streamflow using a stereo computer vision system

Crowd sensing and spatiotemporal analysis in urban open space using multi‐viewpoint geotagged videos

A review of video‐based rainfall measurement methods

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