## 5 Comparative Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Video Argument** & \begin{tabular}{c} **Original** \\ **Video** \\ \end{tabular} & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** \\ \hline \multirow{2}{*}{Object size range} & \multirow{2}{*}{\(5\times 5\)} & \(1\times 1\sim\) & \multirow{2}{*}{\(5\times 5\)} & \multirow{2}{*}{\(5\times 5\)} & \multirow{2}{*}{\(5\times 5\)} & \multirow{2}{*}{\(5\times 5\)} \\  & & & & & & \\ \hline Object brightness range & 0 & 0 & 0 \(\sim\) 0.15 & 0 & 0 & 0 \\ \hline Object velocity range (pixels per second) & 250 & 250 & 250 & 0 \(\sim\) 300 & 250 & 250 \\ \hline Background velocity range (pixels per second) & 150 & 150 & 150 & 150 & 100 \(\sim\) 200 & 100 \(\sim\) 200 \\ \hline \multirow{2}{*}{Movement orientation of background} & \begin{tabular}{c} Towards \\ the right \\ \end{tabular} & \begin{tabular}{c} Towards \\ the right \\ \end{tabular} & \begin{tabular}{c} Towards \\ the right \\ \end{tabular} & \begin{tabular}{c} Towards \\ the right \\ \end{tabular} & \begin{tabular}{c} Towards \\ the right \\ \end{tabular} & \
\begin{tabular}{c} Towards \\ the right \\ \end{tabular} &
\begin{tabular}{c} Towards \\ the left \\ \end{tabular} \\ \hline Background picture & Figure 7a & Figure 7a & Figure 7a & Figure 7a & Figure 7a & Figure 7a & Figure 7a \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters of the generated data. As presented in Figure 16, we also present the ROC curve of the contrastive model to identify true small objects in various complex movement scenarios. Figure 16a,c,e presents a representative frame from each of three video sequences, wherein a small object can be observed moving within a complex, real-world background. The arrows \(V_{B}\) and \(V_{T}\) signify the movement orientations of the dynamic backdrop and the true object, respectively. Figure 16b,d,f depicts the ROC curves for four models, each tasked with the detection of small objects within complex and fluctuating background circumstances. Our proposed visual neural network exhibits superior performance in comparison to the other three models. In addition, we also present the effectiveness of the \(\mathcal{CISTMD}\) model to detect small objects in real datasets. Figure 17 shows the different image frames of real datasets and the de  
Figure 15: (**a**) The receiver operating characteristic (ROC) curve of the \(\mathcal{CISTMD}\) model applied to the initial test video sequence. (**b–f**) The ROC curves that detail the outcomes of four different methodologies on the original sequence across multiple object size variations, brightness scales, motion velocities, and background motion velocities, with a constant false alarm rate of 5.  
tection performance of four models in detecting small objects in real datasets. Figure 17a-c shows image frames 300 and 600 of three different real datasets and depicts the ROC curves of comparison models when applied to a variety of real-world datasets. The results demonstrate that the \(\mathcal{CI}\)STMD model exhibits superior detection performance compared to the ESTMD, DSTMD, and \(\mathcal{F}\)STMD models, while maintaining the same false alarm rate. Figure 16: (a,c,e) A sample frame from each of three test video sequences. A small moving object can be observed against a complex, real-world background. The arrows \(V_{B}\) and \(V_{T}\) signify the movement orientation of the background and object, respectively. (a) Synthetic video one; (c) synthetic video two; (e) synthetic video three. (b,d,f) The receiver operating characteristic (ROC) curves illustrating the performance of four different methods on three test video sequences. We further conducted comparative experiments of our model with methods such as optical flow, background subtraction, feedback STMD [20], fractional-order STMD [22], and apg-STMD [23].