# Experimental Solution for Estimating Pedestrian Locations from UAV Imagery
## 5 Results
### Object Detection Testing  
During the testing of object detection, several different algorithms were selected and compared under various conditions. These data provided useful feedback for the implementation of the proposed experimental solution. The main algorithms considered were YOLO [8] and mask R-CNN [18]. The YOLO algorithm was tested with various input blob resolutions, which highly affected the speed and detection count of the algorithm.  
Table 1 includes the results of testing given the main tested configurations. The detection was performed from distances of around 30 to 50 m and an image resolution of 1920 \(\times\) 1080 pixels. The results show an increase in detection relative to the increase in input resolution, as well as an increase in the image processing time. This is to be expected, as higher resolution inputs contain more data to iterate through. Interestingly, the detection efficiency stagnates and falls after about 1248 \(\times\) 1248 input resolution. Furthermore, mask R-CNN has shown a lower detection to time ratio than YOLO. Even at input blob resolutions of 608 \(\times\) 608 pixels, YOLO achieves not only more true positive detections but also lower processing times than mask R-CNN.  
Another set of testing was performed on video data with shorter detection distances of 10 to 30 m. Additionally, the video recording contained dense groups of people. This provided an efficient method of testing the accuracies in highly populated areas, as shown in Table 2. Several observations were made; firstly, the YOLO algorithm continued to outperform mask R-CNN in the detection to processing time ratio. Similarly, the number of detections increased steadily with the increase in input blob resolution. However, unlike during the detection from higher distances, the resolution of 2496 \(\times\) 2496 pixels had an  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Algorithm** & **True Positive** & **False Positive** & **False Negative** & **Processing Time** \\ \hline YOLO 2496 \(\times\) 2496 & 15 & 0 & 9 & 28.67 s \\ \hline YOLO 1248 \(\times\) 1248 & 23 & 1 & 1 & 7.72 s \\ \hline YOLO 608 \(\times\) 608 & 11 & 0 & 13 & 2.03 s \\ \hline YOLO 416 \(\times\) 416 & 6 & 0 & 18 & 1.13 s \\ \hline Mask R-CNN & 9 & 0 & 15 & 4.75 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Pedestrian detection from distance. Resolution 1920 \(\times\) 1080. Number of pedestrians 24.  
Figure 6: Cluster analysis using K-means algorithm.  
increase in detection compared to lower resolutions. Despite this, YOLO 1248 \(\times\) 1248 continued to outperform other configurations in accuracy, while maintaining high detection speeds.  
The YOLO [8] algorithm proved to be highly efficient in detection under all of the tested conditions. The blob input resolution of 1248 \(\times\) 1248 proved to provide the highest efficiency in terms of detection count and processing time ratio. The configuration was selected during the experimental system implementation. Figure 7 shows the application of the selected configuration on an image containing a large cluster of pedestrians from short and high distances respectively.