## 5 Results
### Object Tracking Evaluation  
Figure 6 shows an example of the qualitative evaluation of the ByteTrack tracking method. The method uses the object detector described in Section 3.4.1. In the first frame (Figure 6a), there are four object detections: {id:39, class: Tree, score:0.70}, {id:41, class: street light, score:0.99}, {id:36, class: Trash bin, score:0.70}, {id:40, class: Tree, score:0.99}. Each detection has a unique id, the class name, the detection score, and the bounding box coordinates which are omitted in this example. In the second frame (Figure 6b) there are three object detections: {id:39, class: Tree, score:0.54}, {id:36, class: Trash bin, score:0.70}, {id:42, class: Tree, score:0.79}. Objects with ids 40 and 41 are no longer visible and new Tree detection (42) is no longer occluded. Note that the confidence score for tree 39 dropped to 0.54. This fluctuation is usual for objects that are far from the camera, but generally, the confidence is higher when the objects are closer. This is visible in frames Figure 6c,d, where the confidence score for tree 39 is 0.92 and 0.95, respectively. In the frame in Figure 6c, a recycle bin is detected with id 43 and a score of 0.95. In the next frame, the recycle bin is even closer and obtains a detection score of 0.99.  
In most scenes, the tracking process demonstrates high effectiveness. However, there are cases where tracking fails to correctly track the objects especially when there are many objects that appear next to each other or when the camera travels at high-speed leading to substantial discrepancies among consecutive frames. Such cases are not frequent in the collected dataset.  
To evaluate tracking performance, we annotate a short video clip and evaluate the tracking quantitatively in terms of the F1 score. Additionally, to investigate the impact of the speed camera on the tracking method, we subsample the number of acquired frames to emulate various car speeds and investigate the impact of speed on the overall accuracy of the tracking approach. Specifically, the original video has 30 FPS and the camera speed is traveling at 30 km/h. Reducing the number of frames with a specific rate increases the emulated speed for that rate. Using this sub-sampling approach we evaluate the tracking methods at various speeds in terms of F1 score. The F1 score is calculated using the tracked objects and the annotated objects. Figure 7 shows the evaluation results. We observe that the performance is above 0.9 F1 for speeds up to 90 km/h. The performance starts dropping significantly for higher speeds. The best score is observed at 30 km/h with an F1 score of 0.921. From the Figure, we can conclude that the performance does not drop significantly for the speed of 50 km/h, which is often the upper limit of speed in most urban areas, but a performance drop is expected for speeds above 90 km/h. We also note that this experiment does not account for motion blurring that might be introduced from the camera at very high speeds, which will further hinder performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Class** & **AP** \\ \hline license plates & 0.947 \\ trees & 0.857 \\ trash bins & 0.964 \\ recycling bins & 0.948 \\ street lights & 0.841 \\ shops & 0.754 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Average precision of each object class at 0.7 IoU threshold.