# A Fast Dynamic Dim Target Tracking Approach for UAVs Using Improved Lightweight Siamese Networks
## 4 Simulation and Experiment
### Experiment
(**a**) car in night; (**b**) house in mountain; (**c**) car in road (high FOV); (**d**) car in road (low FOV); (**e**) target with occlusion; (**f**) target (no occlusion); (**g**) house in lake (close distance); (**h**) house in lake (remote distance). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Category** & **Name** & **Parameters** & **Application** \\ \hline \multirow{6}{*}{Power system} & \multirow{6}{*}{Motor} & Stator size: 100 \(\times\) 33 mm & \multirow{6}{*}{Driving the UAV to fly} \\  & & KV value: 48 RPM/V & \\  & & Motor power: 4000 \(\times\)/rotor & \\  & & Capacity: 2453 mAh & \\  & & Voltage: 7.38 V & \\  & & Temperature: 5–40 \({}^{\circ}\)C & \\  & & Diameter: 54 inch & \\  & & Rotor number: 8 & \\ \hline Operating system & Linux & Linux\_18.04 & The system in Sever \\ \hline \multirow{6}{*}{Software} & Python3 & Pycharm Community Edition 2021 & Developing various algorithms \\  & Matlab & Matlab 2018 b & Data processing \\ \cline{1-1}  & DJI Fly & Android 6.0 & Display image transmission data \\ \hline \hline \end{tabular}
\end{table} 
Table 5: _Cont._The average accuracy and tracking precision of the proposed ILSN algorithm for each group of video sequences are shown in Figure 9. It can be seen that the proposed algorithm maintains an extremely high tracking accuracy and precision under eight video sequences. In scenario 4, the algorithm has the highest accuracy and precision. The average accuracy is over 85% and the average precision is 1.5 pixels. From Table 6, it can be seen that the ILSN successfully completes the tracking of 8 sets of video sequences, with an average tracking accuracy of 86.5% and an average tracking accuracy of 1.8 pixels. In video sequence a, the tracking accuracy and precision produced a slight decrease due to the tracking difficulty of large changes in lighting in front and behind the target vehicle. In video sequence f, there is no severe degradation in tracking accuracy or tracking precision, even in the special case where the target is obscured by trees. The actual situation of ILSN's tracking in sequence f of Figure 9 is shown in Figure 10. For recognition accuracy, the proposed method, which is relatively stable within the frame rate of 20 fps-110 fps, has a relatively large jump in accuracy when the frame rate is too high or low, which indicates that the best performance frame rate for ILSN does not exceed 110. However, precision has remained relatively stable. ILSN has an average tracking accuracy of 86% and an average tracking center of 1.9 pixels in the entire tracking process. By establishing a physical experimental platform, the accuracy and precision of the proposed ILSN target tracking algorithm for UAV tracking of dim targets are verified. It is proved that the algorithm is feasible. It can effectively track dim targets with tiny target sizes, complex background interference, target occlusion, and difficult target camera movement. Meanwhile, ISLN has a tracking speed of 108 frames per second, which is a slight decrease relative to the simulated system. However, it can meet the basic real-time requirements. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number** & **Target Type** & \begin{tabular}{c} **Tracking** \\ **Completed** \\ \end{tabular} & \begin{tabular}{c} **Average** \\ **Accuracy** \\ \end{tabular} & \
\begin{tabular}{c} **Average** \\ **Precision** \\ \end{tabular} &
\begin{tabular}{c} **Tracking** \\ **Speed** \\ \end{tabular} \\ \hline a & Car & Y & 0.78 & 1.04 & 110.2 \\ b & Building & Y & 0.87 & 3.08 & 107.8 \\ c & Car & Y & 0.86 & 1.20 & 107.5 \\ d & Car & Y & 0.91 & 4.14 & 110.2 \\ e & Car & Y & 0.86 & 1.73 & 108.8 \\ f & Building & Y & 0.89 & 1.85 & 108.5 \\ g & Building & Y & 0.86 & 1.59 & 107.4 \\ h & Building & Y & 0.85 & 2.45 & 109.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Algorithm performance in 8 scenarios. Figure 9: Algorithm performance in 8 scenarios.