# Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping
## 1 Introduction  
Pointer meters are used to measure temperature, pressure, flow, level and digital display. Compared with digital instruments, the pointer meters have the advantages of reliable structure, low cost and intuitive readability. Especially in industrial measurement such as substation automation or petrochemical industry, the pointer meters are more likely to observe rapid changes in measured values. Therefore, operators often use the pointer meters to observe measurement process. In general, the readings of the pointer meters are obtained by person judging. With the development of automation and intelligent technology, industry pays attention to that the computer vision methods measure the pointer meter readings. Although modern imaging techniques [1; 2; 3; 4] have increased image resolution, the measuring accuracy is still affected by several factors, such as angle or distance of the camera observing the meters. Therefore, it is essential to utilize the computer vision techniques to measure the pointer meter in order to improve the accuracy and convenience of measurement. Generally, present methods of the pointer meter measurement can be divided into two kinds of models. One is extracting the critical points of measured meters display to calibrate the reading. In 1994, [PERSON] et al. [5] detected image of the circular arc scales to obtain values of a meter using Hough transform. And the values on the rectangle frame were obtained visually by detecting the two lines. In 2000, [PERSON] [6] proposed combining image processing with computer vision techniques to automatically measure the readings of analog instruments, improving measurement stability compared to human eye discrimination reading. Using edge extraction and pattern recognition technology, [PERSON] et al. [7] dealt with images of the meters to achieve automatically reading of the power meter data in 2005. An automatic verification system [8] of dial indicators was investigated by the image subtraction and dynamic threshold segmentation of features of the dashboard image in 2008. Using the edge detection and mathematical morphology, [PERSON] [9] screened out image region in the pointers, and then pointer positions were located by the structure and position characteristics in 2009. A modified active shape models scheme [10] for automatic measuring of multiple analog metershad been proposed in 2014. The analog meter reading scheme utilized different feature points, and appropriately modeled of its displacement according to analog meter scales. In 2018, [PERSON] [11] proposed to use the analysis based on the binarization threshold to segment the pointer region, solving the problem of the pointer shadow. And a random sample consensus method put forward to overcome background image interference. The other model uses a parametric perspective imaging model to convert the pixel coordinates to object space locations. At the Centre for Metrology and Accreditation in Finland, [PERSON] et al. [12] came up with computer vision based device for calibrating meter readings in 2002, and calculated the measurement uncertainty. In 2007, they also updated the equipment developed to a micrometer calibration device [13]. By the Canny edge detection, [PERSON] [14] investigated a vision system for both calibrating the instruments and obtaining the readings in 2008. Using the computer vision, [PERSON] et al. [15] presented a model for calibrating portable measurement devices in 2009. In order to automatically calibrate analog meter measurement process, two segmentation-free methods were presented by [PERSON] [16] in 2013. Bresenham algorithm and Radial projection were applied to identify the location of the pointer of the analog meter so that the readings of the analog meter can be automatically obtained. In 2014, an automatic identification model for pointer meters based on binocular vision was developed by [PERSON]. Use two cameras located on the left and right side of the pointer meter in the model.