# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results
### Moving Vehicle Tracking Test  
To verify the effectiveness of DTSTC during the tracking phase, it was tested for four scenarios. The visualization results are shown in Figure 13, with different colored boxes representing different tracked objects. Figure 13a shows the results of long-distance tracking for a single vehicle. The blue box in the diagram tracks the target with good results over long distances, no ID switching occurs during tracking, and the tracking box position is more accurate. Figure 13b shows the vehicle tracking results obtained using a right-angle bend. The red box tracks the target steadily through the bend without ID switching, and the vehicle color remains similar to the background without losing the target. Figure 13c shows the more intensive multivehicle tracking results, where the proposed algorithm tracks stably, with no significant false detections, missed detections, or ID switching. Figure 13d shows the vehicle tracking results with partial occlusion. Although the tree branches occluded the vehicle, the tracking results were good, with no ID switching or missed detections. Thus, the proposed tracking algorithm has a more robust tracking performance  
Figure 11: Recall rates comparison chart.  
Figure 10: Precision comparison chart.  
in complex environments such as long sequences, non-linear, dense multiple targets, and partial occlusion.  
A small area within each area was intercepted for testing to further verify the effectiveness of the data association method. The experiments compared the CMOGv2 approach combined with the data association approach (CMOGv2_D), the detection approach combined with the SORT algorithm (C_SORT), and DTSTC. In Table 2, the optimal values are indicated in bold. The proposed method is more stable in most areas, multitarget tracking precision remains high, tracker identity maintenance is excellent, and the number of ID switches is low. The method of tracking data associations using SORT was the next most effective, maintaining a high overall level. However, the method using CMOGv2 detection was less effective. Notable among these is the low precision of multitarget tracking for each method in Area 2. This is because missed detections occurred during the detection phase, resulting in certain targets not being tracked during the tracking phase. In Area 3, the results of the proposed method and the method of tracking data associations using SORT were free of omissions and misdetections. This is because there is less traffic in the area, vehicle contrast is evident, and excellent detection provides good initial values for tracking.  
Figure 12: Precision and recall stability assessment results.  
In summary, DTSTC used in this study can effectively correlate inter-frame vehicles in satellite videos. However, tracking precision largely depends on the detection effect. Although the detection method in DTSTC has largely improved the detection precision and recall rate, the phenomenon of missed detection still exists, which leads to ID switching or missed targets in the tracking targets.