# TuSeSy: An Intelligent Turntable Servo System for Tracking Aircraft and Parachutes Automatically
## 5 Experiment
### Impact of Frame Difference and Background Subtraction  
According to reference [11], which presents 29 kinds of background subsections, we selected 5 outstanding algorithms--DPWrenGABGS, MixtureOfGuassianV1 BGS, MultiLayerBGS, PixelBasedAdaptiveSegmenter, and LBAAdaptiveSOM--to present a contrast experiment with general frame differences when segmenting the foreground (aircraft and parachutes) from the background (the sky). Each video had the same threshold, which was used to maximize the F2-score. We utilized the semi-automatic calibration method to obtain the F2-score. Tables 1 and 2 show the average of the F2-score and FPS from all frames in each video.  
Figure 17a-e presents the foreground segmented from video2 using DPWrenGABGS, MixtureOfGuassianV1 BGS, MultiLayerBGS, PixelBasedAdaptiveSegmenter, and LBAAdaptiveSOM, respectively. Figure 17f is the foreground segmented from video2 with the general frame difference method. Figure 17a-d demonstrate that DPWrenGABGS, MultiLayerBGS, and PixelBasedAdaptiveSegmenter can segment the foreground of the plane perfectly; however, with the turntable rotating continuously, the light intensity of the background changes constantly (rapidly weakens); the update of the background may be delayed (\(124_{th},326_{th},727_{th}\) frame). Figure 17b shows that MixtureOfGuassianV1 BGS cannot entirely segment the foreground.  
\begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Video** & **DPWrenGABGS** & **MixtureOfGuassianV1 BGS** & **MultiLayerBGS** & **PixelBasedAdaptiveSegmenter** & **LBAAdaptiveSOM** & **TuSeSy** \\ \hline Video1 & - & 87.72 & 5.27 & 14.97 & - & 253.43 \\ \hline Video2 & 80.00 & 84.74 & 4.73 & 17.18 & 27.74 & 243.90 \\ \hline Video3 & 79.37 & 85.00 & 6.31 & 18.38 & - & 252.34 \\ \hline Video4 & 83.33 & 86.34 & 6.66 & 18.34 & - & 254.12 \\ \hline \hline \end{tabular}
\end{table}
Table 2: FPS of all measures (‘-’ represents failure in obtaining the result).  
Figure 17: From top to bottom are (**a–f**), and from left to right are the foreground with the bounding box at different moments in video2.  
Figure 17e implies that although LBAadaptiveSOM uses the first 52 frames as learning sets, in the following segment, there are still existing problems, e.g., the foreground is incomplete or inaccurate and the bounding box cannot frame the target completely or correctly. As for Figure 17f, we can conclude that the general frame difference can segment the foreground well in each frame with good adaptive capacity to the change of the light intensity. Since the control signal of the turntable can make the tracking target as far as possible in the center of each frame, the target in each frame will not experience serious ghosting. In addition, the bounding box can fit the target border well. Furthermore, Tables 1 and 2 also show that the general frame difference has the largest F2-score and highest FPS. MultiLayerBGS has a relatively high F2-score in contrast to other background differences; however, it has the lowest FPS. The foregrounds from video1 and video4 are similar to video2. Video3 segmented the foreground well with the background of clouds, using the general frame difference, which has a good F2-score and a high FPS (Figure 18).