## 3 System Overview
### ROI Management
#### 3.2.3 ROI Adjustment
\begin{table}
\begin{tabular}{c c} \hline \hline
**Video Name** & **Proposed Algorithm** \\
**Compared to YOLOv7** \\ \hline
0 & 315.86\% \\ \hline
1 & 54.17\% \\ \hline kaggle02 & 86.98\% \\ \hline Youtube & 201.49\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection speed improvement. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Video Name** & **YOLOv7** & **Proposed Algorithm** & **Compression Rate** \\ \hline
0 & 182.9 MB & 34.7 MB & 81.03\% \\ \hline
1 & 85.2 MB & 12.1 MB & 85.8\% \\ \hline kaggle02 & 115.1 MB & 52.7 MB & 54.21\% \\ \hline Youtube & 46.7 MB & 29.4 MB & 37.04\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: File size of video processed by YOLOv7 and the proposed system. of the UAV identifier in a heatmap. As shown on the scale to the right, the brighter the color of the tile, the more frequently the identifier shows respected results. The diagonal tiles on the heatmap represent cases where the prediction was correct, while other tiles represent cases where the prediction was incorrect. Diagonal values refer to the number of correct predictions during the testing phase. Therefore, the higher the diagonal values of the confusion matrix, the more accurate the UAV identifier is. As shown in Figure 6, most of the test trials are clustered around the diagonal tiles, meaning that the classifier performs desirably. Figure 7 shows the Area Under the ROC Curve (AUC) [36], giving a graphical representation of the performance of the UAV identifier. The AUC of the ROC curve is a widely accepted metric for evaluating the performance of a classification model [37]. The ROC curve describes the performance of a binary classifier based on its discrimination threshold. On the AUC graph, the x-axis represents the true positive rate of the classifier, while the y-axis represents the false positive rate of the classifier. Since the UAV identifier is a 16-class classifier, merely applying outputs of the UAV identifier would not be able to be diagnosed. Therefore, it will be fed a scoring label representing the cases in which the identifier correctly predicted the class. Each ROC curve in the graph refers to the classifier's performance on one of the 16 classes. The ROC-AUC score--ranging from 0 to 1--was 0.9869. A score of 0.5 is the equivalence of random guessing, and a score of 1 indicates that the model is an omniscient classifier. The dash line in the graph represents the score of 0.5. This indicates that the UAV identifier is performing near to its best. Figure 6: Confusion-matrix of the UAV identifier in a heatmap. ## 5 Conclusions

In this paper, an accurate UAV tracking/surveillance system with an efficient encoding method was designed. The proposed system uses an ROI-based approach to apply two different compression rates on a single video. On every frame of the input video, the ROI region and the non-ROI region are determined using UAV detection, and a lower compression rate is applied to the ROI region while a higher compression rate is applied to the non-ROI region. This way, non-ROI regions, holding relatively less important information, can be compressed without the concern of decoding. The movement of ROI is managed using optical flow. Efficient transmission is achieved by applying XROI-DCT for effective compression. The evaluation, composed of the implemented prototype, shows that the proposed system can successfully encode with two different compression rates. The evaluation shows that the videos encoded with the proposed system experienced an average of 70% reduced file size without hindering the UAV region. The reduced file size of the encoded video shows that it requires less bandwidth, and the fact that ROI is located on the bounding box of the leading object detector, YOLOv7, most of the time shows that detection is applied accurately. For our future work, we plan to apply a rate decision to our system. By applying a rate decision system, we can adapt the rate of a whole UAV tracking stream system.