## 5 Experiments and Results
### Experimental Results
All twelve aircraft in the image were accurately detected and located with purple bounding boxes. As shown in Figure 12b, the twelve detected aircraft were treated as targets that needed to be tracked in the following frames. They were assigned different identification numbers and were located using different colored bounding boxes. In subsequent frames, each aircraft was tracked, and its activity status was given based on its velocity measurement. At the beginning, the top eight aircraft (Aircraft 0\(\sim\)Aircraft 7) were parked, and the bottom four aircraft (Aircraft 8\(\sim\)Aircraft 11) were queuing to take off (as shown in Figure 12c). Later, the first aircraft on the runway took off, and the next aircraft turned around and was ready to take off (Figure 12d). At the end of the video, two taxiing aircraft enter from the left side of the field of view (Figure 12e,f). Figure 13 shows the speed vs. time curves for every tracked aircraft in Video 2, similar to Figure 11. Curves of fourteen different colors represent the fourteen different aircraft targets in Video 2. At the 25 th second, the algorithm obtains the speed measurement of the newly entered Aircraft 12. Similarly, the algorithm obtains the speed measurement of the newly entered Aircraft 13 at the 27 th second. Figure 11: Speed measurements for each aircraft in test Video 1. Figure 12: Screenshots of the experimental results from test Video 2. (**a**) Aircraft detection results in the first frame of the video; (**b**) Accrafts 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**) The aircraft is stable tracked even with complex environmental backgrounds, lighting conditions, and various movements of the aircraft, such as turning (Aircraft 8); (**e**) A newly entering aircraft (Aircraft 12) is also identified and stably tracked; (**f**) Another entering aircraft (Aircraft 13) is identified and stably tracked. From the experimental results in Video 2, we can conclude that the algorithm has the following advantages. Firstly, the algorithm supports the stable tracking of turning aircraft (e.g., Aircraft 8) due to the improved rotation matching method. Secondly, the algorithm has strong robustness, even under complex lighting conditions. As shown in Figure 11(c), there was a great deal of light reflected on the surface of Aircraft 8, but the algorithm can still track it stably. Lastly, the algorithm supports the detection of newly entered aircraft. As shown in Figure 11(e),f, the newly entered Aircraft 12 and 13 were accurately detected. They were then accurately tracked, and their activity status was identified. We also tested and compared the time-consumption of the algorithm on an RTX2080 Ti-based server and on the embedded AI computing unit called Jetson AGX Xavier. Table 3 outlines the time-consuming statistics of the two experiments on different platforms. We evaluated the time consumption of the two main parts of the algorithm separately. DCNN-based aircraft detection takes approximately 4 s and 20 s on the server and embedded AI unit, respectively. Aircraft tracking and activity recognition takes less than 200 ms on both computing platforms. Video 2, which contains more aircraft targets, consumed more time. Compared with the traditional ground processing methods, the method in this paper significantly improves the efficiency of aircraft activity recognition and reduces the amount of satellite-to-ground data transmission. Although there is a delay of tens of seconds, the method in this paper can serve as an important reference for the exploration of on-board intelligent processing and satellite intelligent decision-making.