# Deep Learning-Based Consistent Object Detection in Distance on Highly Perspective Region
## 3 Generation of a Virtual Video and Appearance Attributes of the Moving Vehicles
### Generation of a Virtual CCTV Video  
The WCS and OI coordinate system on the image plane of tunnel CCTV are depicted in Figure 5. In the WCS, the tunnel CCTV image is taken at the point 'O' which is the location of a CCTV installation. The WCS is represented by X, Y and Z, and the vehicle's movement on the ground plane is directed into the negative Z axis. The vertical dimension corresponds to the Y axis, while the lateral directions are aligned with the X axis. On the other hand, the image plane on the CCTV is represented in the two-dimensional OI coordinate system on which the horizontal and vertical directions are denoted by x axis and the y axis, respectively. In this context, the virtual tunnel is conceptualized as a straight, one-way, two-lane tunnel with a width of 12.6 m, a height of 6.2 m and a length of 5 km. Additionally, virtual tunnels were created in a straight tunnel by using a collection of modeled road data called the Low Poly Road Pack [33].  
Based on the WCS, the specification of the virtual CCTV installation specification is outlined in Table 1. The CCTV is located at the height of 11 m along the \(Z\) axis. Regarding the CCTV rotation angles, \(\alpha\), \(\beta\), and \(\gamma\), these correspond, respectively, to rotations around the X, Y, and \(Z\) axis. The selected rotation angles are calibrated to achieve a resemblance with the actual tunnel image in Figure 1b.  
This study involves the creation of two distinct videos. The creation of the two videos is achieved through the Recorder Window, which is one of UNITY's plugins, and the video is created and saved by the installed CCTV in CCTV viewing direction. The first video portrays a single vehicle in motion, aiming to identify the appearance velocity (AV) and changes in the viewing size attributed to the vehicle objects on the image plane. The second video depicts multiple vehicles in motion, undertaken to facilitate a comparative analysis of the OD performance through the use of deep learning models. Initially, the single vehicle motion video features a vehicle traveling at a constant speed of 100 km/h. This vehicle possesses dimensions resembling those of a standard passenger car, being 4.2 m in length, 2.2 m in width, and 1.4 m in height. This vehicle, as well as the vehicles in the multi-vehicle motion video described below, utilized Low Poly Cars [34], which is a collection of pre-modeled vehicle data.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Value** \\ \hline Focal length (mm) & 18 \\ Sensor size (mm) & 36 \(\times\) 24 \\ CCTV location (X, Y, Z) & 5 m, 4 m, 11 m \\ CCTV rotation angle (\(\alpha\), \(\beta\), \(\gamma\)) & 0\({}^{\circ}\),212\({}^{\circ}\), 0\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specification of the virtual tunnel CCTV.  
Figure 5: WCS and OI coordinate system in a 3D virtual tunnel site.  
Conversely, for the multi-vehicle motion video, nine vehicles are incorporated, consisting of one bus, two trucks and six passenger cars, all maintaining a constant velocity. Detailed specifications for this video are presented in Table 2. The video resolution is specified in Table 2, and is characterized by dimensions W (width) and H (height).  
The specifications for each vehicle object are shown in Table A1 of Appendix A and are modeled to resemble real-world vehicles; we intended to avoid any influencing factors other than the vehicle viewing distance. Furthermore, the shape of each object can be seen in Figure A1, and utilizes different colors. For the multi-vehicle video scenario, all vehicles are basically driving at a constant speed. Then, as shown in Figure A2, there are three patterns in a single video, which appear within a time interval of about 3 s. The speeds of the vehicles in each pattern are set up as shown in Figure A2a-c to create a realistic traffic flow.