# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results
### Comparative Testing and Quantitative Analysis of Moving Vehicle Inspection
As shown in the chart, DTSTC has the best detection accuracy in most cases, it being only slightly lower than CMOGv2 in Area 6. The recall rates were low in Area 2 under the influence of harsh environmental factors, while the rest of the area maintained high levels. To further validate the stability of the algorithm, Figure 12 shows the variations in the precision and recall for each area over 100 frames. The detection precision of DTSTC remained high in all areas and varied smoothly between the frames. There are more noticeable fluctuations in the recall, but they still perform better than the other methods. Therefore, DTSTC is not only better in terms of average detection performance, but also exhibits good results in terms of detection stability. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Test Data** & **Area 1** & **Area 2** & **Area 3** & **Area 4** & **Area 5** & **Area 6** \\ \hline \multirow{3}{*}{TD} & \(P\) (\%) & 72.56 & 38.81 & 67.21 & 56.72 & 68.35 & 24.52 \\  & \(r\) (\%) & **89.45** & 77.88 & 96.01 & 89.21 & 86.08 & 91.22 \\  & \(F\) (\%) & 79.90 & 51.65 & 78.80 & 68.63 & 74.73 & 39.49 \\ \hline \multirow{3}{*}{MOGv2} & \(P\) (\%) & 68.70 & 36.06 & 67.43 & 61.13 & 69.61 & 39.40 \\  & \(r\) (\%) & 80.40 & 67.81 & 95.63 & 89.70 & 83.70 & 82.67 \\  & \(F\) (\%) & 73.63 & 46.52 & 78.85 & 71.31 & 74.72 & 51.24 \\ \hline \multirow{3}{*}{ViBE} & \(P\) (\%) & 81.47 & 38.33 & 91.59 & 63.38 & 72.24 & 72.85 \\  & \(r\) (\%) & 75.78 & 71.84 & 86.16 & 70.66 & 65.61 & 82.74 \\  & \(F\) (\%) & 78.23 & 49.42 & 88.74 & 66.02 & 68.52 & 77.36 \\ \hline \multirow{3}{*}{CMOGv2} & \(P\) (\%) & 93.17 & 92.50 & 87.51 & 94.77 & 87.44 & **93.76** \\  & \(r\) (\%) & 80.40 & 67.48 & 85.88 & 87.79 & 83.70 & 82.67 \\  & \(F\) (\%) & 85.88 & 77.63 & 86.79 & 91.04 & 85.16 & 87.85 \\ \hline \multirow{3}{*}{MVDSV} & \(P\) (\%) & 98.37 & 87.94 & 81.37 & 90.08 & 84.41 & 89.40 \\  & \(r\) (\%) & 55.43 & 51.48 & 79.03 & 75.93 & 61.77 & 80.28 \\  & \(F\) (\%) & 70.59 & 64.67 & 80.02 & 81.57 & 71.05 & 83.96 \\ \hline \multirow{3}{*}{VOMVD} & \(P\) (\%) & 94.80 & **96.45** & 86.82 & 92.21 & 91.26 & 80.43 \\  & \(r\) (\%) & 75.78 & 71.83 & 86.17 & 70.66 & 65.61 & 82.73 \\ \cline{1-1}  & \(F\) (\%) & 84.00 & 82.31 & 85.82 & 79.65 & 75.99 & 81.35 \\ \hline \multirow{3}{*}{DTSTC} & \(P\) (\%) & **98.86** & 96.01 & **91.69** & **99.99** & **91.84** & 88.75 \\ \cline{1-1}  & \(r\) (\%) & 84.00 & **79.24** & **96.05** & **92.44** & **87.26** & **97.15** \\ \cline{1-1}  & \(F\) (\%) & **90.03** & **86.80** & **93.28** & **95.96** & **89.22** & **92.47** \\ \hline \hline \end{tabular} Bold text in the table indicates the best value and underlined text indicates the second best value.  
\end{table} 
Table 1: Results of different algorithms for vehicle detection. By combining the results of each area detection for the satellite video data, we find that the temporal difference and MOGv2, despite maintaining a good recall rate, are subject to serious interference and have a low precision rate. Although ViBE performs more consistently, the overall precision is low, and there is significant \"ghosting\" and missed detections. CMOGv2 showed significant improvement in precision but still had a high rate of missed detections. Although MVDSV improves detection accuracy through heat map constraints, the recall performance is mediocre, which is probably due to the instability of the constraint construction when analyzed. The VOMVD performed well overall, but there is still room for improvement in the recall rate. In contrast, DTSTC achieved better results in terms of both detection precision and recall, with the highest F1 score. Therefore, the detection algorithm in DTSTC can also perform well in the face of the complex background and noise of satellite video data and has certain advantages in terms of robustness and applicability.