# Computer Vision-Based Approach for Reading Analog Multimeter
## 6 Application of Monitoring Liquid Nitrogen Meters  
By suitably modifying our algorithms, the proposed approach can be applied for reading the similar kinds of A-meters. In this section, we demonstrate a successful application of monitoring a storage level meter and pressure meter installed on a 15 m\({}^{3}\) liquid nitrogen (LN2) tank which is located at outside our laboratory, as shown in Figure 23a. These two analog needle-type meters are very significant for our laboratory and need to be checked frequently to guarantee that the storage level and pressure of tank are maintained within a safe range. For example, excessive pressure will damage the solenoid valve of testing equipment and cause other dangerous situations, and once the LN2 storage level is below the low limit, the technician should call for LN2 filling to avoid sudden interruption of testing in laboratory. Reading such an analog meter based on human eyes is rather inefficient and will easily cause fatigue problems during long-term monitoring. Therefore, our computer vision approach is a valuable way to deal with such a scenario. Figure 21: Reading the A-meter fails for the camera placed at H as indicated in Figure 20.  
Figure 22: Reading the A-meter fails for the camera placed at I as indicated in Figure 20.  
In this application, an Internet Protocol (IP) camera (model AXIS P3364-LVE 12 mm [24]) was installed to acquire the video stream (with a frame resolution of \(1280\times 960\) pixels and frame rate 30 frames/sec) including both the level and pressure meters, as shown in Figure 23b. At night, the LN2 tank meters can be illuminated by a road lamp and a LED flood light as shown in Figure 23c. For each frame, the subimage of the level meter and pressure meter is extracted first according to the predefined ROIs, and then processed individually by our method to read the value indicated by the meter pointer. Note here that only the pointer's angle detection (presented in Section 3) was implemented in this application. Figure 24 illustrates one frame containing the level and pressure meters from a video stream at rainy night. Using our monitoring system, the results shown in Figure 25 demonstrate the usefulness of our method in this application. Even during worst-case scenarios, such as rainy nights, our system can work well, as shown by the results in Figure 26.  
Figure 23: (**a**) A 15 m\({}^{3}\) Liquid nitrogen (LN2) tank setup at outside our laboratory. (**b**) The two significant meters installed on the LN2 tank are the level meter (top) and pressure meter (bottom), and an Internet Protocol (IP) camera is used to monitor these two meters. (**c**) The LN2 tank meters are illuminated by a road lamp and a LED floodlight at night. Figure 24: One frame containing the level and pressure meters from a video stream on a rainy night. Since the storage level of LN2 gradually decreases during consumption in the laboratory and increases during filling, the pointers shown on the two meters normally vary very slowly, as the real video (recorded in rainy day) demonstrated in [25], and the pressure should be maintained within a range between 3 to 5 bar, and the storage of LN2 should be maintained above 80, as indicated on the scale used by our laboratory. In order to show the capability of reading the LN2 meters by our method, we intentionally set the pointer of meter moving fast by manual as the real video demonstrated in [26]. The two videos given in [25; 26] demonstrate that our system can read the two dials on the LN2 tank in a real application with an execution time of about 0.2\(\sim\)0.6 second for each reading. This can be regarded as real-time processing, compared to the very slow change of the meter pointer. As a result, such a non-invasive computer vision system provides us a convenient alternative to monitor the LN2 tank information without installing additional expensive digital sensors and truly helps us to guarantee the safety of using LN2.