## 7 Measurement Data Reports  
Measurements was carried out on several devices with the following characteristics: focal length 2,38 (mm), image height 3120 (px), and sensor height 2,21 (mm). Presented in Table 5, the measurements  
Figure 10: An mCOP recon view RARV demonstrating the reporting capabilities used by the user-adjustment sliders to input height and object size into the preview (1: slider to input object height; 2: slider to fit grid with viewed object; 3: cropping grid; 4: relative distance to object; 5: object height; 6: size of cropping grid). (1-6) were conducted under field conditions where camouflaged soldier recognition and identification was performed. The device was held by an operator 1.80 m in height at his eye level. As can be seen, Method A had much more accuracy in studies 1-6. This was due to the inaccurate identification of distance difference between two measurements, which is generally hard to do in field conditions. The confirmation for this was the difference in accuracy between the results obtained in measurement 4 and the others. In this case (4), the distance difference was accurately measured, but in other cases it was only estimated. Measurements 7-8 were conducted under laboratory conditions at small distances, and the observed object's size was \(10\times 10\) cm. The reference measurements were taken with a stabilized smartphone and measuring tape. In those tests, a similar level of precision was obtained (Table 6). The inaccuracy of the field studies was mainly caused by the imprecise representation of introduced (reference) parameters such as distance difference (for Method B). Another factor that increased the measurement error was the imprecise cropping of the reported object (performed by the application's operator). The operator was required to capture the reported object on the measuring grid: the more accurate this procedure is when performed, the better the resolution of measurements that are achieved will be. Moreover, the size of the measuring grid is a key component of the calculations and thus the distortion evaluation and mechanisms for serial snapshots. An additional solution for the problem of inaccurate boxing of an object is the application of image processing libraries, which can automatically create and measure a bounding box over a central object. The utilization of machine learning methods can support automatic object detection and capture in order to provide accurate battlespace object identification and measurement. In order to measure objects over 30 m away, image stabilization is necessary. Simulation was carried out with Sigma 150-600 f/5-6.3 DG OS HSM lens: focal length: 600 [mm], image height [px] 3120 [px], sensor height 4.62 [mm], object height 4 [m]. In order to maximize precision of measurement, it is proposed to use coupled with smartphone optical system. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{2}{c}{**Operation Delays**} & \multicolumn{2}{c}{**Measurement Method A**} \\ \hline
**No.** & \(H_{obj}^{p}\) (px) & **Operation Time (s)** & \(H_{obj}^{p}\) (px) & **Distance D (m)** \\ \hline
1 & 900 & 3.02 & 800 & 2025.97 \\
2 & 800 & 3.16 & 900 & 1800.87 \\
3 & 700 & 3.42 & 1000 & 1620.78 \\
4 & 600 & 3.81 & 1001 & 1619.16 \\
5 & 500 & 4.24 & 1002 & 1617.55 \\
6 & 400 & 4.93 & 1003 & 1615.93 \\
7 & 300 & 5.87 & 1200 & 1350.65 \\
8 & 200 & 7.08 & 1500 & 1080.52 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Measurement-specific data evaluated for measurement delays and distance.