# Satellite Video Moving Vehicle Detection and Tracking Based on Spatiotemporal Characteristics
## 4 Results
### Comparative Testing and Quantitative Analysis of Moving Vehicle Inspection  
To verify the effectiveness of the proposed method in the moving vehicle detection phase, several of the best motion target detection algorithms developed in recent years were selected for comparison tests on the experimental data. The comparison methods include temporal difference (TD), an improved mixture of Gaussian v2 (MOGv2), ViBE, the MOGv2 algorithm based on the constraints in this study (CMOGv2), the method presented in Literature 6 (MVDSV), and the method presented in Literature 8 (VOMVD). phenomena of \"double shadow\" and \"ghostly shadows\" occur for large targets without texture, as shown in Figure 9e-6. Although MVDSV partly removes the interference of noise in the background, as shown in Figure 9f-7, there are missed detections, as shown in Figure 9f-5,f-6. The VOMVD detection target is the presence of ghosting, as shown in Figure 9g-5, as well as missed detection (Figure 9g-6). In contrast, DTSTC eliminates the influence of pseudo-motion targets and background noise with very few missed detections, as shown in Figure 9h-7. There are no \"double shadows\" for larger targets, as shown in Figure 9h-5.h-6. Therefore, DTSTC has a significant advantage over traditional methods in terms of visualization. To quantify and analyze the detection results, \(P\), \(r\), and F1 score were calculated separately for each algorithm within each area. As shown in Table 1, bold text indicates optimal values and underlined text indicates suboptimal values. Within Area 1, DTSTC achieves the highest detection precision. The recall of this method was slightly lower than that of temporal difference. The analysis suggests that this may be due to the low threshold setting for the inter-frame differencing method, which increases the detection recall at the expense of lower detection precision (no distinction between noise and moving targets). Within Area 2, DTSTC had the highest recall and F1 scores. The accuracy was slightly lower than that of VOMVD; however, the recall was considerably higher than that of VOMVD. Comparison tests show that the accuracy of the method with constraints is considerably higher than that of the conventional method, which is analyzed because the background of Area 2 contains a large lake, and the diffuse reflection of the  
Figure 9: Comparison diagram for Area 1 moving vehicle detection, where columns **(1)**–(**4**) are the detection results in Area 1 for frames 30, 50, 70, and 82, respectively. Columns **(5)** and **(6)** show the road area refinement maps. Column **(7)** shows the refinement of the background area. The yellow box in the diagram shows the detected moving vehicles. For ease of description in the subsequent text, the names of the methods are replaced by **(a–h)**. lake causes a lot of noise interference. The constraint effectively eliminates this type of background interference. The best results in terms of precision, recall, and F1 score were achieved in Areas 3-5. This is because there is less noise interference in these areas and a greater degree of road and vehicle disparity, providing better initial conditions for moving target detection. Spatiotemporal domain constraints build complete and contrast-enhanced assisted detection, greatly enhancing detection capabilities and enabling the robust detection of vehicles in motion satellite videos. Within Area 6, both DTSTC and CMOGv2 performed well. DTSTC was slightly less accurate than CMOGv2 in terms of detection precision. The reasons for this are as follows: On the one hand, it may be that the MOGv2 misdetection phenomenon causes it to have no misdetection noise; therefore, the phenomenon of low recall and high precision occurs. On the other hand, it may be that the contrast enhancement algorithm in DTSTC increased the influence of noise within the road, resulting in more mis-detected targets. Figures 10 and 11 provide statistics on the detection precision and recall of each algorithm for different areas.