# Real-Time Motion Detection Network Based on Single Linear Bottleneck and Pooling Compensation
## 4 Experiments
### Ablation Studies

Table 4: Experimental results of channel reduction factor ablation for our single linear bottleneck. The last row represents the average result for each scenario. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{2}{c}{**Baseline**} & **Bottleneck [37]** & \multicolumn{2}{c}{**Invert LB [24]**} & \multicolumn{2}{c}{**Our**} \\ \cline{2-9}  & \(\mathcal{F}\) & \(\mathcal{PWC}\) & \(\mathcal{F}\) & \(\mathcal{PWC}\) & \(\mathcal{F}\) & \(\mathcal{PVC}\) & \(\mathcal{F}\) & \(\mathcal{PWC}\) \\ \hline baseline & 0.9914 & 0.0421 & 0.9752 & 0.0962 & 0.9798 & 0.0541 & 0.9767 & 0.0624 \\ cameraJit & 0.9780 & 0.2246 & 0.9595 & 0.3254 & 0.9684 & 0.2846 & 0.9688 & 0.2514 \\ badWeather & 0.9785 & 0.0464 & 0.9577 & 0.0621 & 0.9652 & 0.0511 & 0.9641 & 0.0602 \\ dynamicBg & 0.9652 & 0.0238 & 0.9510 & 0.0522 & 0.9621 & 0.0324 & 0.9635 & 0.0298 \\ intermitt & 0.8798 & 0.0886 & 0.8242 & 0.1898 & 0.8320 & 0.1012 & 0.8284 & 0.1403 \\ lowFrameR. & 0.9248 & 0.0521 & 0.0155 & 0.0658 & 0.9263 & 0.0597 & 0.9247 & 0.0625 \\ nightVid. & 0.9612 & 0.1014 & 0.9456 & 0.1526 & 0.9514 & 0.1113 & 0.9507 & 0.1287 \\ PTZ & 0.9910 & 0.0122 & 0.9724 & 0.0624 & 0.9801 & 0.0243 & 0.9814 & 0.0195 \\ shadow & 0.9622 & 0.1052 & 0.9430 & 0.1224 & 0.9506 & 0.1123 & 0.9  
second best method, FgSegNet_v2 [9], our MDNet-LBPC had significant gains of 123 on inference frames per second, respectively. Meanwhile, our MDNet-LBPC is comparable with the state-of-the-art methods in terms of accuracy metrics. **Qualitative Results. ** Figure 5 (\"Input\" and \"ground truth\" adapted from [47]) shows the qualitative results, with several top performance methods taken from the CDNet2014 benchmark [47]. Our MDNet-LBPC detection effect is comparable to similar methods. **Segmentation Stability Comparison. ** We used F-measure and classification error percentage (PWC) to analyze the stability performance of 12 methods in each category of the dataset from both positive and negative aspects. It can be seen that the curve corresponding to our method is one of the top curves in the whole curve comparison diagram. The model segmentation accuracy belongs to one of the top few methods in the current CDNet2014 [47] data set, indicating that this method has excellent segmentation performance. Compared with the curve at the bottom of the figure (the segmentation accuracy is unstable), the curve corresponding to the model in this paper changes gently, which indicates that our method has good stability for video segmentation of different scenes. For the percentage of misclassification (PWC), we counted the PWC of each method in each category of the dataset, and plotted its curve with the scene as shown in Figure 7. The vertical axis represents PWC and the horizontal axis represents each scene. It is shown in the figure that among the methods with better performance on the current CDNet2014 [47] data set, the curve corresponding to the method in this paper is one of the lowest ones in the whole curve comparison diagram, indicating that the model segmentation error rate is low and the curve change is also gentle. This proves the segmentation accuracy and stability of the model with respect to classification accuracy. Therefore, the experiment shows that our model not only has high segmentation accuracy, but also has good segmentation stability, and can achieve accurate segmentation of various scenes.