Measurements of rainfall rates from videos

Dong, Rong; Liao, Juan; Li, Bo; Zhou, Huiyu; Crookes, Danny

doi:10.1109/cisp-bmei.2017.8302066

Cited by 15 publications

(19 citation statements)

References 36 publications

(27 reference statements)

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“…Outside this range, however, the blur effect causes the size of raindrops to be overestimated. Thus, following Dong et al (), this study considers only the focused raindrops within the DoF, where the blur effect is ignorable; the unfocused rain streaks are removed. Hence, the theoretical sampling volume V (m 3 ) is the volume of the truncated rectangular pyramid between the two light gray planes in Figure b, that is,

V = \frac{1}{3 \times 10^{9}} \frac{d_{f} - f}{d_{f} \cdot f} w_{s} h_{s} ((), s_{f}^{3} - s_{n}^{3}) .

…”

Section: Methodsmentioning

confidence: 99%

“…The focal length and F‐number of the lens are 4 mm and 2.2, respectively. Based on preliminary tests and previous studies (Allamano et al, ; Dong et al, ), the focus distance was fixed to 1.0 m to achieve an appropriate DoF, resulting in a near‐focus distance of s n = 0.57 m and a far‐focus distance of s f = 4.29 m. The frame rate was configured to capture one frame per second to achieve a balance between the computational costs of processing and the temporal resolution of the estimated rainfall intensity. The camera is rotatable such that three different backgrounds can be examined (see Figure ).…”

Section: Methodsmentioning

confidence: 99%

“…Nonetheless, this strategy requires a high‐quality image recorder (see Table ), which is unlikely to be available in crowdsourced situations. Similarly, Dong et al's () identification algorithm is also temporally based, but it uses a gray tone criterion (a value derived from grayscale) for selecting candidate rain streaks instead of grayscale. Similar to our approach, Dong's estimation step discards the unfocused rain streaks.…”

Section: Accuracy Of Rainfall Measurementsmentioning

confidence: 99%

“…Vision-based rainfall gauging is still in an exploratory phase. To the best of our knowledge, only two recent studies have reported similar approaches (Allamano et al, 2015;Dong et al, 2017). The differences between our proposed approach and the existing methods are summarized in Table 4.…”

Section: Comparison With Existing Approachesmentioning

confidence: 99%

“…However, the algorithm is applicable only in ideal conditions (i.e., stationary camera, static background, and almost linear change of background brightness) and is unsuitable for complicated and dynamic backgrounds. Recently, Dong et al () proposed a more robust approach to identifying rain streaks and deriving the rainfall rate using a gray tone function that makes the grayscale subtraction nonlinear. Nonetheless, its measurements are not very accurate; consequently, the method is applicable in only limited scenarios.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Jiang

Babovic

Zheng

et al. 2019

Water Resources Research

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“Opportunistic sensing” represents an appealing idea for collecting unconventional data with broad spatial coverage and high resolution, but few studies have explored its feasibility in hydrology. This study develops a novel approach to measuring rainfall intensity in real‐world conditions based on videos acquired by ordinary surveillance cameras. The proposed approach employs a convex optimization algorithm to effectively decompose a rainy image into two layers: a pure rain‐streak layer and a rain‐free background layer, where the rain streaks represent the motion blur of falling raindrops. Then, it estimates the instantaneous rainfall intensity via geometrical optics and photographic analyses. We investigated the effectiveness and robustness of our approach through synthetic numerical experiments and field tests. The major findings are as follows. First, the decomposition‐based identification algorithm can effectively recognize rain streaks from complex backgrounds with many disturbances. Compared to existing algorithms that consider only the temporal changes in grayscale between frames, the new algorithm successfully prevents false identifications by considering the intrinsic visual properties of rain streaks. Second, the proposed approach demonstrates satisfactory estimation accuracy and is robust across a wide range of rainfall intensities. The proposed approach has a mean absolute percentage error of 21.8%, which is significantly lower than those of existing approaches reported in the literature even though our approach was applied to a more complicated scene acquired using a lower‐quality device. Overall, the proposed low‐cost, high‐accuracy approach to vision‐based rain gauging significantly enhances the possibility of using existing surveillance camera networks to perform opportunistic hydrology sensing.

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V = \frac{1}{3 \times 10^{9}} \frac{d_{f} - f}{d_{f} \cdot f} w_{s} h_{s} ((), s_{f}^{3} - s_{n}^{3}) .

…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Accuracy Of Rainfall Measurementsmentioning

confidence: 99%

Section: Comparison With Existing Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Jiang

Babovic

Zheng

et al. 2019

Water Resources Research

View full text Add to dashboard Cite

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Degradation of Commercially Available Digital Camera Images due to Variation of Rainfall Intensity in Outdoor Conditions

Kanazawa

Uchida

2022

Preprint

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Camera-based rainfall observation is a useful technology that contributes to the densification of rainfall observation networks because it can measure rainfall with high spatio-temporal resolution and low cost. To develop of practical camera-based rainfall observation technology, using the extinction coefficient as a clue, this study proposed relational Equations representing the relationship between image information, rainfall intensity, and scene depth by linking the theoretically derived rainfall intensity with a technique proposed in the computer vision field for removing static weather effects. Then, the proposed Equations were applied to outdoor images taken by commercial interval cameras at the observation site in a mountainous watershed in Japan. As a result, it was confirmed that transmission calculated from the image information decreases exponentially according to the increase in rainfall intensity and scene depth, as assumed in the proposed Equations. Therefore, the proposed Equations are generally valid even for outdoor images, and extremely important findings that will improve camera-based rainfall observation techniques were obtained. On the other hand, the calculated extinction coefficient tended to be overestimated in patches with a small scene depth, and overestimation of the extinction coefficient due to aerosol effects was also observed in the images taken during no rainfall. Although there are issues at present that need to be resolved for the technology proposed in this study, this technology has the potential to help the development of a camera-based rainfall observation technology that is accurate, robust, versatile, and accessible.

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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

show abstract

Measurements of rainfall rates from videos

Cited by 15 publications

References 36 publications

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Degradation of Commercially Available Digital Camera Images due to Variation of Rainfall Intensity in Outdoor Conditions

A review of video‐based rainfall measurement methods

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