# Autonomous Driving Assistance with Dynamic Objects Using Traffic Surveillance Cameras
## 4 Results Evaluation  
In the proposed method, the mapping of moving objects such as vehicles and people to exact positions on the spatial coordinate system in the traffic surveillance camera image is a very important factor. Figure 8 shows the target object identification results in successive frames. For the object location detection method, a minimum square shape was adopted, and the square for each tracked object was used as location information of the object for visualization and registration. Therefore, the verification of the degree of position matching is required, and for this purpose, the coordinate system conversion accuracy was evaluated based on the process to examine the degree of coincidence between any point on the main path in the traffic surveillance camera image and the converted world conversion point. So, we proposed that it uses a virtual primary trajectory on the HD map, which is the center line of the road or the lane. The error is measured between a virtual primary trajectory from the HD map and the sample points of the estimated trajectory from the image. The virtual primary trajectory is a reference line used as the critical path. Figure 8: Results of object detection from the traffic surveillance camera. Figure 6: Definition of a homography matrix for projection (not real world). Figure 7: Process for error estimation in the global coordinate system. At the beginning, in the map structure with precision built with actual measurement, the critical path (\(L\)) was obtained as shown in Figure 9. It is between lanes and guides the vehicle in the direction it should drive at the junction, branch, intersection, etc., of roads, and the traffic surveillance camera. A lane is found in the image, and \(n\)-random points are sampled by calculating the image-based main path located in the center between the lanes. For sampling, lane extraction and main route calculation were performed with an evaluation approach and an image processing library to ensure accuracy [32]. In this way, by using the coordinate system transformation method of Section 2 for the points sampled from the traffic surveillance camera image, it was converted to a point \(P^{\prime}_{i}=\left(x^{\prime}_{i},\ y^{\prime}_{i}\right)\) of the spatial coordinate system of the map with precision, and the vertical intersection \(P^{*}_{i}=\left(x^{*}_{i},\ y^{*}_{i}\right)\) that is closest to and located at the correspondence position was found. The estimated error is as follows:  
\[Err=\left|P^{\prime}-P^{*}\right| \tag{2}\]  
If the coordinate system transformation is performed well without error, the calculated point is located on the map's main path with precision. In other words, it can be said that the closer it is to zero, the higher the degree of position matching. Figure 9 shows the structure of the map with precision defined by lanes (black line) and a middle line (red line). Figure 9 shows random points sampled for evaluation in the main path of the traffic surveillance camera image corresponding to the map with precision. In this study, after sampling 26 random points, the degree of localization of the proposed method was evaluated with this process. As shown Figure 10, we used virtual points to verify the proposed method from the HD map. The results of comparing the transformed and vertical intersections, and the distance between the two points are provided in Appendix A. As a result of the distance comparison, errors occurred from as small as 0.02 m to as large as 0.48 m, and the average (Avg) and root mean square (RMS) showed errors of 0.15 m and 0.19 m, respectively.