## 5 Comparative Analysis
The experimental results are shown in Table 3. When \(F_{A}=5\), our model demonstrates outstanding performance in detection, with an average detection rate of 0.81, which is significantly higher than that of other methods (all below 0.7). This indicates that the \(\mathcal{CI}\)STMD visual network has stronger robustness in detecting small target movements in complex dynamic environments. Finally, we compared the precision and F1 scores of different models in Tables 4 and 5. The results show that our model performs excellently in suppressing background false positives, with an average precision as high as 0.09, which is significantly higher than the average level of other models (all below 0.05). In addition, our model also shows an advantage in terms of F1 score. Our average F1 score is higher than 0.1, reaching 0.1705, while the F1 scores of other methods are all below 0.1. This indicates that our model has a better ability to identify moving targets. The above experiments indicate that the \(\mathcal{CI}\)STMD model is more robust and stable when dealing with complex and varying background environments and different types of targets. Figure 17: Representative frames extracted from three real-world datasets and the receiver operating characteristic (ROC) curves illustrating the performance of four different methods on three real-world datasets. (**a**) Real video one; (**b**) real video two; (**c**) real video three. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Video** & **Background** & **Model** & **Model Flow** & **DSTMD** & **ESTMD** & \(\mathcal{F}\)**STMD** & **Feedback STMD** & **Frac-STMD** & **app-STMD** & \(\mathcal{C}\)**_ZSTMD** \\ \hline Initial video & 0.0010 & 0.0007 & 0.0094 & 0.0088 & 0.0086 & 0.0066 & 0.0231 & 0.1014 \\ Synthetic video one & 0.0011 & 0.0008 & 0.0011 & 0.0004 & 0.0094 & 0.0065 & 0.0290 & 0.0646 & 0.1273 \\ Synthetic video two & 0.0011 & 0.0009 & 0.0088 & 0.0051 & 0.0099 & 0.0081 & 0.0185 & 0.0129 & 0.0634 \\ Synthetic video three & 0.0011 & 0.001 & 0.0204 & 0.0163 & 0.0163 & 0.0178 & 0.1547 & 0.0150 & 0.1384 \\ Real video one & 0.0021 & 0.0005 & 0.0021 & 0.0004 & 0.0040 & 0.0028 & 0.0231 & 0.0144 \\ Real video two & 0.0020 & 0.0008 & 0.0029 & 0.0244 & 0.0241 & 0.0247 & 0.0204 & 0.0029 \\ Real video three & 0.0009 & 0.0007 & 0.0230 & 0.0273 & 0.0269 & 0.0226 & 0.0566 & 0.0195 & 0.0663 \\ \hline Mean & 0.0012 & 0.0007 & 0.0138 & 0.0140 & 0.0138 & 0.0135 & 0.0480 & 0.0172 & 0.0048 \\ \hline \end{tabular}
\end{table} 
Table 4: Precision of different methods. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline
**Methods** & **Background** & **Optical Flow** & **DSTMD** & **ESTMD** & \(\mathcal{F}\)**STMD** & **Feedback STMD** & **Frac-STMD** & **app-STMD** & \(\mathcal{C}\)**_ZSTMD** \\ \hline Initial video & 0.049 & 0.04 & 0.36 & 0.50 & 0.57 & 0.66 & 0.55 & 0.76 & 0.67 \\ Synthetic video one & 0.50 & 0.44 & 0.60 & 0.69 & 0.71 & 0.71 & 0.73 & 0.57 & 0.89 \\ Synthetic video one & 0.50 & 0.43 & 0.57 & 0.73 & 0.74 & 0.77 & 0.74 & 0.75 & 0.81 \\ Synthetic video three & 0.52 & 0.50 & 0.7 & 0.78 & 0.74 & 0.83 & 0.59 & 0.57 & 1.1 \\ Real video one & 0.44 & 0.46 & 0.08 & 0.25 & 0.28 & 0.49 & 0.07 & 0.18 & 0.56 \\ Real video one & 0.51 & 0.45 & 0.4 & 0.55 & 0.53 & 0.32 & 0.33 & 0.37 & 0.25 \\ Real video three & 0.58 & 0.48 & 0.33 & 0.82 & 0.81 & 0.43 & 0.56 & 0.49 & 0.80 \\ \hline Mean & 0.51 & 0.46 & 0.43 & 0.62 & 0.56 & 0.56 & 0.51 & 0.52 & 0.81 \\ \hline \end{tabular}
\end{table} 
Table 3: Detection rate of different methods (\(F_{A}=5\)). \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Video** & **Background** & **Optical Flow** & **DSTMD** & **ESTMD** & \(\mathcal{F}\)**STMD** & **Feedback STMD** & **Frac-STMD** & **app-STMD** & \(\mathcal{C}\)**_ZSTMD** \\ \hline Initial video & 0.0020 & 0.0164 & 0.0187 & 0.0175 & 0.0170 & 0.0170 & 0.0234 & 0.0451 & 0.1858 \\ Synthetic video one & 0.0022 & 0.00179 & 0.027 & 0.0186 & 0.0156 & 0.0137 & 0.0263 & 0.0488 &