# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### Ablation Experiment  
We conducted ablation experiments on the Occlusion-aware Mask Propagation Network using the MOTS training set. To investigate the optimal performance of linear interpolation, we analyzed the impact of different mask IoU thresholds on the method.  
Since the MOTS20-05 and MOTS20-09 video sequences among the MOTS dataset were captured by a moving camera, the displacement of the same target across consecutive frames is relatively large. In contrast, for the remaining GT data, the mask IoU ratios of the same target in consecutive frames are mostly clustered around 0.9.  
To determine the optimal value for the mask IoU threshold \(\alpha\), we tested the impacts of different \(\alpha\) values on the final results using the MOTS-02 sequence. The experimental results are presented in Table 2.  
It can be observed that when \(\alpha\) = 0.9, the MOTSA, s MOTSA, and (FN + FP) metrics achieve relatively optimal results. Thus, we set the mask IoU threshold \(\alpha\) of the linear interpolation algorithm to 0.9.  
We conducted the ablation experiment on the MOTS training set, with the results shown in Table 3. We define LI as the linear interpolation method, OA as the occlusion-aware sub-module, and MP as the mask propagation network. The comparative results between linear interpolation and mask propagation fully demonstrate the effectiveness of mask propagation in completing trajectories.  
MT stands for Mostly Track targets. It is observed that after adding only the linear interpolation method, the MOTSA and other metrics of the baseline decreased, which means worse tracking performance. However, with the further addition of the occlusion-aware sub-module that identifies the occluded states of targets, the +LI+OA approach shows improvements in both MOTSA and MT metrics compared to the baseline. This proves the effectiveness of the occlusion-aware sub-module. Furthermore, after integrating OA with our proposed Robust Mask Propagation Network, which uses the mask propagation network to complete missing trajectories based on the occlusion-aware module, this method achieved a 0.5% improvement in MOTSA over the baseline and a significant 138% increase in MT. This indicates that the framework produces more complete target trajectories. Additionally, MOTSP improved by 0.3% and 0.4% compared to the baseline and +LI+OA, respectively, further enhancing the accuracy of our method.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Mask IoU Threshold (\(\alpha\))** & **MOTSA \(\uparrow\)** & **sMOTSA \(\uparrow\)** & **(FP + FN) \(\downarrow\)** \\ \hline \(\alpha\) = 0.8 & 63.5 & 45.5 & 2509 \\ \(\alpha\) = 0.85 & 64.9 & 46.8 & 2410 \\ \(\alpha\) = 0.9 & **65.0** & **47.9** & **2392** \\ \(\alpha\) = 0.95 & 64.8 & 46.3 & 2452 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Tracking results with different mask IoU thresholds.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Method** & **MOTSA \(\uparrow\)** & **MOTSP \(\uparrow\)** & **MT \(\uparrow\)** \\ \hline Baseline & 74.9 & 86.7 & 42 \\ +LI & 74.7 & 86.5 & 110 \\ +LI+OA & 75.3 & 86.6 & 105 \\ +OA+MP & **75.4** & **87.0** & **110** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation results on the MOTS training set.