# Point of Interest Recognition and Tracking in Aerial Video during Live Cycling Broadcasts
## 4 Results and Discussion
### Speed Benchmark  
Next to the accuracy of the POI recognition and tracking, the speed of the pipeline is equally important, especially since it is intended for use during live broadcasts. To assess the pipeline's efficiency, the speed is benchmarked on a virtual machine with one Tesla V100 GPU, four 2.7 GHz vCPU cores, and 32 GB RAM. Live tests have shown that faster performance is achievable on dedicated hardware over this virtual machine. The average speed of the main actions in the pipeline was measured over a period of 1 h and 30 min of helicopter video footage, resulting in the timings presented in Table 3. \begin{table}
\begin{tabular}{l c} \hline \hline
**Functionality** & **Using MedianFlow Tracking** & **Using SAM 2 Tracking** \\ \hline GPS range checking & 0.11 ms \(\pm\) 0.02 \(\approx\) 9090 FPS \\ \hline Saliency detection & 91 ms \(\pm\) 60 \(\approx\) 11 FPS \\ Object mask retrieval & 61 ms \(\pm\) 5 \(\approx\) 16 FPS \\ POI recognition & 122 ms \(\pm\) 41 \(\approx\) 8 FPS \\ Total of recognition phase & 274 ms \(\pm\) 106 \(\approx\) 4 FPS \\ \hline POI mask refinement & 136 ms \(\pm\) 87 \(\approx\) 7 FPS \\ Tracker initialization & 441 ms \(\pm\) 418 \(\approx\) 2 FPS & 311 ms \(\pm\) 33 s 3 FPS \\ Total of tracking & 577 ms \(\pm\) 505 \(\approx\) 2 FPS & 447 ms \(\pm\) 120 \(\approx\) 2 FPS \\ preparation phase & 13 ms \(\pm\) 1 \(\approx\) 77 FPS & 364 ms \(\pm\) 36 s 3 FPS \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Speed benchmark results when using either the MedianFlow or SAM 2 tracker. The first 5 steps are equal for both since they are independent of the used tracker. Figure 13: Visualization of the tracking results of the _Vinkemolen_ at the middle of the broadcast time window using (**a**) the MedianFlow tracker (green box = ground truth, blue box = prediction) and (**b**) the SAM 2 tracker (blue mask = prediction). MedianFlow lost track of the windmill because the helicopter was rotating around it. In contrast, SAM 2 handles this rotation perfectly. As anticipated, verifying the distance between the helicopter and the points of interest to determine if any POIs are nearby is a fast operation that can significantly reduce unnecessary computations. In contrast, the POI recognition pipeline is considerably slower, requiring an average of 274 ms per check. However, since our pipeline only performs this recognition phase once per second, the achievable 4 FPS is more than sufficient. If a POI is identified, an additional 136 ms is needed to refine the POI's region. Initializing the tracker also comes with a computational cost, which is different based on the chosen tracker. Here, it is shown that the traditional MedianFlow tracker has a higher initialization cost over the more advanced SAM 2 tracker. Consequently, after a successful POI recognition and initialization of the tracker, the processing will, on average, lag behind the live broadcast with 851 or 721 ms for the MedianFlow or SAM 2 modus, respectively. However, with the MedianFlow tracker only taking about 13 ms per frame, i.e., processing 77 frames per second, this buffer is quickly eliminated. However, our benchmark shows that the SAM 2 tracking is not fast enough to run real-time. Still, the authors of this model report a speed of 30.2 frames per second (FPS), indicating that it is possible to run this model real-time on our video stream of 25 FPS with more powerful, dedicated hardware. The system is designed to maintain consistent speed, even if it scales to more races and POIs. On the one hand, this is because the GPS location check is efficient, regardless of the number of POIs that need to be verified. This initial step effectively filters out unnecessary computations when no POIs are within range. On the other hand, the majority of the methodology is independent of the number of nearby POIs. The recognition step is the only component directly influenced by this. However, this rarely poses a challenge as most scenarios involve only one nearby POI, with occasional instances of up to three.