# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 4 Experiments
### Ablation Studies  
In this section, we investigate the important setups and components of MDNet-LBPC. The model without pooling compensation and single linear bottleneck branch of MDNet-LBPC is used as the baseline model. * Single Linear Bottleneck. Ablation studies of single linear bottlenecks include reduction factor comparison experiments and bottleneck structure comparison studies. **Comparison of different reduction factors. ** We set different reduction factors to transform channels from 512 to 512, 256, 128 and 64 for comparison. The results are shown in Table 4. When the channels are transformed to 512 and 256, the experimental results are not significantly worse, while the segmentation accuracy of the model is significantly reduced when the number of channels is changed from 256 to 128 and from 128 to 64. Meanwhile, the classification error rate is significantly increased. **Comparison of different bottleneck structures. ** The results of the three bottlenecks on the baseline model are shown in Tables 5 and 6. The inverse residual linear bottleneck (Invert LB) [24] does not perform channel reduction, and the reduction factor of our single linear bottleneck (Single LB) and bottleneck [37] is 0.5. It can be seen that all bottlenecks lead to a slight decrease in model segmentation accuracy. The acceleration improvement of our single linear bottleneck can outperform that of the linear bottleneck by 5%, so it is more suitable for improving the model inference efficiency. \begin{table} \begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Resolution**} & \multicolumn{2}{c}{**Baseline**} & **Bottleneck [37]** & **Invert LB [24]** & **Single LB** & **Polling C.** \\ \cline{2-6}  & \(\mathcal{FPS}\) & \(\mathcal{FPS}\) & \(\mathcal{FPS}\) & \(\mathcal{FPS}\) & \(\mathcal{FPS}\) \\ \hline \(320\times 240\) & 66 & 77 & 143 & **154** & 63 \\ \(360\times 240\) & 59 & 68 & 128 & **138** & 57 \\ \(640\times 480\) & 20 & 23 & 39 & **42** & 19 \\ \(720\times 480\) & 18 & 20 & 35 & **38** & 18 \\ \hline \hline \end{tabular}   
* Pooling compensation. The experimental results of pooling compensation ablation are shown in Figure 4, Tables 6 and 7. After adding the pooling compensation mechanism, the model can better segment the foreground targets with complex edge contours. In most categories, \(\mathcal{F}\) scores are improved, and \(\mathcal{PVC}\) was decreased. The average \(\mathcal{F}\) of the 11 categories increased from 96.06% to 97.11%, and the average \(\mathcal{PVC}\) decreased from 7.5% to 5.01% with comparable inference efficiency. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**512**} & \multicolumn{2}{c}{**256**} & \multicolumn{2}{c}{**128**} & \multicolumn{2}{c}{**64**} \\ \cline{2-9}  & \(\mathcal{F}\) & \(\mathcal{PWC}\) & \(\mathcal{F}\) & \(\mathcal{PVWC}\) & \(\mathcal{F}\) & \(\mathcal{PWC}\) & \(\mathcal{F}\) & \(\mathcal{PWC}\) \\ \hline baseline & 0.9784 & 0.0617 & 0.9767 & 0.0624 & 0.9108 & 0.1005 & 0.8541 & 0.2128 \\ cameraJit & 0.9701 & 0.2412 & 0.9688 & 0.2514 & 0.9247 & 0.2914 & 0.8599 & 0.3621 \\ badWeather & 0.9632 & 0.0588 & 0.9641 & 0.0602 & 0.9065 & 0.1023 & 0.8215 & 0.2218 \\ dynamicBg & 0.9641 & 0.0279 & 0.9635 & 0.0298 & 0.9277 & 0.0589 & 0.8432 & 0.1029 \\ intermitt & 0.8279 & 0.1399 & 0.8284 & 0.1403 & 0.7425 & 0.1521 & 0.5858 & 0.1999 \\ lowFrameR. & 0.9254 & 0.0603 & 0.9247 & 0.0625 & 0.8899 & 0.1014 & 0.7612 & 0.1856 \\ nightVid. & 0.9511 & 0.1014 & 0.9507 & 0.1287 & 0.9003 & 0.1452 & 0.7922 & 0.2123 \\ PTZ & 0.9823 & 0.0181 & 0.9814 & 0.0195 & 0.9321 & 0.0887 & 0.8132 & 0.1767 \\ shadow & 0.9533 & 0.1086 & 0.9528 & 0.1099 & 0.9011 & 0.1424 & 0.8354 & 0.1569 \\ thermal & 0.9636 & 0.1712 & 0.9631 & 0.1721 & 0.9112 & 0.1847 & 0.8011 & 0.2202 \\ turbulence & 0.9562 & 0.0407 & 0.9547 & 0.0438 & 0.9002 & 0.1003 & 0.7878 & 0.2145 \\
**Overall** & **0.9487** & **0.0936** & **0.9481** & **0.0982** & **0.8952** & **0.1334** & **0.7959** & **0.2060** \\ \hline \hline \end{tabular}  
\end{table}