# Real-Time Flood Monitoring with Computer Vision through Edge Computing-Based Internet of Things
## 2 Background and Related Work
### Vision-based Water Level Measurements  
There is a plethora of work on flood monitoring based on camera systems. These range from combining various image processing techniques to determine the water level, to implementing deep neural networks for a higher classification accuracy. Measuring hydrological data under complex illumination conditions such as low lighting, water surface glare and artificial lighting obstructs the accuracy of measurement systems significantly. Image processing tasks can be interfered with when working in the aforementioned conditions, hence requiring more sophisticated/efficient image processing [10], data streaming [11; 12; 13; 14] and network resource management techniques [15; 16; 17; 18]. Some of the related works are summarised as follows. [PERSON] et al. [19] proposed to use near-infrared (NIR) imaging to tackle poor visibility while measuring the water level from a gauge by creating a digital template of the gauge for accurate readings. The conversion from the digital template to coordinates was done by orthorectifying the region of interest (ROI) image of the staff gauge, followed by a horizontal projection to locate the water line and finally converting to the water level with the physical resolution of the digital template. It achieves an accuracy of up to 1 cm. Similarly, [PERSON] et al. [20] proposed a texture and numerical recognition technique to process the staff gauge features. It extracted numerical features from the staff gauge and trained a random forest classifier to determine the level of water, resulting in an overall accuracy of 97%. A cloud-based measurement system was proposed by [PERSON] et al. [21], which also relied on a character recognition algorithm since it depended on a staff gauge to measure the readings. The subsequent technique used a correlation analysis of images and by determining the difference in the correlation coefficient, it identified the level of the water surface. The drawback for these techniques is the dependence on staff gauges to measure the water level, as without it, a digital template cannot be formed, and neither can a character recognition algorithm work. [PERSON] et al. [22] proposed a robust solution that negated camera shivers and jitters. The water level detection was based on an ROI that was selected from the calibration points whereas the line was detected from a set of lines after applying a Hough transform [23]. Based on photogrammetry principles, the water line was calculated with the transformation from the pixel space to the real-world space. The water detection algorithm allowed the system to achieve an accuracy of 1 cm. The significance of this system was that it could detect and convert the water level accurately without a staff gauge due to its robust calibration. Studies without using a water gauge were presented by [PERSON] et al. and [PERSON] et al. [24; 25]. Both studies relied on similar traditional image processing techniques by performing greyscale conversion, contrast adjustment and a binarisation of images. The lines were detected through the Hough transform algorithm performed on thresholded images before transforming the pixel coordinates to metric units. The work presented by [PERSON] et al. yielded poor results in the open environment due to the complex conditions and a low-resolution camera. In another study, [PERSON] et al. [26] proposed a methodology comprised of a web camera integrated with a NIR light and an optical bandpass filter to detect near-infrared light. It also consisted in utilising a staff gauge and the water level was obtained through monocular imaging by observing any abrupt changes in the horizontal projection curve. The novel implementation of a maximum mean difference (MMD) method detected the water level in the pixel space. The MMD was tested against [PERSON]'s thresholding [27] and order statistic filtering (OSF), outperforming the latter two techniques under complex illumination conditions. The implementation yielded an accuracy of up to 1 cm.