## 5 Experiments and Results
### Experimental Results  
Figure 10 shows some screenshots of the experimental results on test video 1. The image shows the end of an airport runway and its adjacent farmland. There are five aircraft in the image. The three aircraft parked at the top of the picture are relatively small in size. Some of the aircraft have tones similar to their nearby environmental backgrounds and are not clearly visible in the image. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Test Video**} & \multicolumn{4}{c}{**Parameters**} \\ \cline{2-5}  & **GSD** & **Image Size** & **Frame Rate** & **Lengt** \\  & **(m)** & **(pixel)** & **(fps)** & **H(s)** \\ \hline Video 1 & 1.13 & 1024 \(\times\) 1024 & 25 & 14 \\ Video 2 & 0.92 & 1100 \(\times\) 1024 & 25 & 28 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of remote sensing videos. Figure 10a shows the aircraft detection results in the start frame. The aircraft were detected with the DCNN-based rotation detector described in Section 3. The five aircraft were all correctly detected in the start frame and located with purple bounding boxes. As shown in Figure 10b, the five detected aircraft were treated as targets that needed to be tracked in the following frames. These aircraft were assigned different identification numbers and were located using different colored bounding boxes. In subsequent frames, the algorithm tracked the five aircraft using image matching and measured their speed to identify the type of activity. The measured speed and recognized activity status of each aircraft were updated every 25 frames. The current activity status of the aircraft was displayed using different colors: parking was displayed in yellow, taxiing was displayed in green, and flying was displayed in blue. As shown in Figure 10c,d, all five aircraft were continuously and correctly tracked. Three aircraft (Aircraft 2, 3, and 4) that were parked on the tarmac were correctly identified to be parking. Aircraft 0, taxiing on the runway, was correctly recognized to be taxiing. Aircraft 1 was correctly recognized to be flying. Figure 11 shows the speed vs. time curves for every tracked aircraft in Video 1. Curves of five different colors represent the five different aircraft targets. The two horizontal dashed lines in the figure represent the speed thresholds used to divide the three different  
Figure 10: Screenshots of the experimental results from test Video 1. (**a**) Aircraft detection results in the first frame of the video; (**b**) Aircrafts detected in the starting frame are all tracked stably; (**c**) The speed of each tracked aircraft is measured and from this the type of aircraft activity is determined; (**d**) Each aircraft is stably tracked and its type of activity is determined. aircraft activities. When the aircraft speed is in the lowest zone, the state of the aircraft is parking. When the aircraft speed is in the middle zone, the state of the aircraft is taxiing. When the aircraft speed is in the upper zone, the state of the aircraft is flying. From the experimental test results in Video 1, the algorithm is identified as having the following advantages. Firstly, the algorithm supports the reliable detection and stable tracking of weak and small aircraft targets (e.g., Aircraft 4). Secondly, the algorithm can stably track aircraft at different speeds. Thirdly, the algorithm can accurately track aircraft targets under complex and changeable backgrounds (e.g., Aircraft 1). Lastly, based on the aircraft tracking and speed measurement, the algorithm can further identify the current activity status of the aircraft. In Figure 12, some screenshots of the experimental results from Video 2 are displayed. The images show the end of an airport runway and its adjacent apron. There are more aircraft in the remote sensing images, and they are of various shapes and sizes. In addition, the motion of the aircraft in the remote sensing images is more complex. Figure 12a shows the aircraft detection results in the start frame.