# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### POI Tracking  
The tracking performance of the system is validated at three moments during the broadcasted time window: the start, middle, and end. Table 2 presents the mean IoU scores across all points of interest at these moments using both trackers. Additionally, the impact of the object refinement step is presented. When using the MedianFlow tracker, the results demonstrate that the refinement improves the tracking IoU on average, with increases of 8%, 17%, and 0.6% for the start, middle, and end, respectively. However, it is also noted that tracking performance tends to decline towards the end of the POI broadcast time. The refined bounding boxes maintain better tracking performance for a longer duration compared with the non-refined boxes, as indicated by the middle scores, but ultimately, both approaches see a drop to approximately 40% by the end of the tracking period. This is due to this traditional tracker not being able to handle rotational movements well. This limitation is clearly solved by using SAM 2 tracking. The IoU scores are not only higher overall compared with MedianFlow, they are also more consistent from the start to the end, with a refined IoU of about 65% across the board. The performance gain by the refinement step is less steep, but still decent as it helps to reduce some segmentation mistakes. Note that these are IoU scores for the inner bounding box of the tracked mask to make them comparable with the traditional tracker. This makes their results appear worse, while in practice, the tracked masks are often nearly perfect. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Metric** & **Using MedianFlow Tracking** & **Using SAM 2 Tracking** \\ \hline Precision & & 78.6\% \\ Recall & & 80\% \\ Mean start offset & & \(-\)15.3 s \\ Mean end offset & 5.8 s & 11.1 s \\ Mean completeness & 95.3\% & 95.3\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: POI recognition and tracking results when using either the MedianFlow or SAM 2 tracker. The precision, recall, and mean start offset are purely based on the recognition phase, so these results cannot differ between trackers. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Mean IoU Using MedianFlow Tracking**} & \multicolumn{2}{c}{**Mean IoU Using SAM 2 Tracking**} \\
**SAM** & **Start** & **Middle** & **End** & **Start** & **Middle** & **End** \\ \hline Initial & 48.5\% & 39.9\% & 39.4\% & 62.9\% & 61.5\% & 61.0\% \\ Refined & 55.4\% & 46.8\% & 41.8\% & 66.8\% & 65.7\% & 65.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: POI tracking results when using either the MedianFlow or SAM 2 tracker. For MedianFlow, the IoU between the ground truth and tracked inner bounding box is calculated. For SAM 2, the inner bounding box is first derived based on the tracked mask in order to provide IoU results that are comparable with the other tracker. Next, IoU scores for the tracking based on both the initial and refined mask are given to evaluate the effectiveness of this processing step. Appendix B provides insights in the individual tracking results for each POI, of which some of the results will be discussed in detail. For instance, the _Sint-Martinuskerk_ achieves high tracking scores after refinement, with most of the IoU scores over 75% for both trackers. Figure 10(a) illustrates that the tracked region using the MedianFlow approach is indeed good. One outlier is the SAM 2 tracker that starts with an IoU of 61%. However, this is due to the calculation of the inner bounding box, whereas the underlying tracked mask is nearly perfect, as can be seen in Figure 10(b). However, lower IoU scores do not necessarily indicate unusable tracking. For example, the MedianFlow tracking of the _Kartuizerpriorij_ starts with an IoU of 67% and drops to a score of 30%. Figure 11(a) reveals that this is due to not poor tracking but rather a change in camera angle. The tracking begins when the camera is facing the POI diagonally, resulting in a smaller inner bounding box.