# Occlusion-Robust Multi-Target Tracking and Segmentation Framework with Mask Enhancement
## 4 Experiments
### Experiment Results  
This subsection evaluates the proposed MOTS framework on the MOTS test set. Table 4 presents a comparison with state-of-the-art methods in multi-target tracking and segmentation, where our approach outperforms in MOTSA, MT, and FN metrics. For the multi-object tracking and segmentation trajectory association algorithms compared in Table 4, the principles of the trackers are analyzed as follows. ReMOTS_[45] enhances tracking components through detection fusion and adaptive thresholding. EMNT [46] introduces a general recurrent tracking unit with multi-hypothesis framework for association modeling. GMPHD_MAF [10] incorporates pedestrian re-identification classifiers to filter outliers before trajectory association. MPNTrackSeg [47] employs differentiable message-passing networks based on network flow models. TrackRCNN [1] extends Mask R-CNN [32] with tracking branches using bipartite graph matching. Our method detects occluded states by analyzing regression trends, extracts spatial-temporal features through mask propagation network, and achieves multi-target tracking and segmentation using a masked-enhanced MHT algorithm. Notably, our method achieves a 0.7% higher MOTSA (84.4%) compared to the second-best tracker and reduces false negatives (FNs) by 6.1% (3765 vs. 3999). The results prove the framework's superior accuracy in occluded scenarios. This improvement stems from the two key innovations: the mask propagation network's ability to recover occluded targets by leveraging temporal coherence in spatial features and the fusion of mask-based association metrics with motion cues. The significant reduction in FN demonstrates the effectiveness of our mask propagation network, as the framework bridges gaps caused by transient occlusions successfully. EMNT achieves higher IDF1 compared to our method (77.0% vs. our 75.1%), but its reliance on sequential feature updates leads to 15% higher FN (4326) in long-term occlusions, underscoring the advantage of our parallel mask-and-motion fusion design. Our method exhibits a trade-off in FP, primarily due to the sensitivity of mask propagation to abrupt motion changes in dynamic scenes. This limitation arises from the linear interpolation assumption in occluded regions, which may fail under rapid camera or target movements. Future work could integrate adaptive motion modeling to mitigate this issue. The tracking framework proposed in these experiments shows that it can associate masks across frames accurately and achieves target tracking and segmentation while adapting to various scenarios, which contains severe occlusion scenarios by design. Our competitive sMOTSA (70.2%) and IDF1 (75.1%) scores, approaching those of SOTA performance (ReMOTS_: 70.4%; sMOTSA, 75.0% IDF1), validate the robustness of our method under heavy occlusion conditions. Tests conducted on public datasets have verified the effectiveness of this mask-enhanced framework. To demonstrate the effectiveness of the proposed multi-object tracking and segmentation framework directly, Figure 4 shows the partial tracking results of our method on the \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Tracker** & **MOTS\(\uparrow\)** & **IDF1\(\uparrow\)** & **sMOTSA\(\uparrow\)** & **MOTS\(\uparrow\)** & **MT\(\uparrow\)** & **TP \(\downarrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** \\ \hline
**TrackRCNN**[1] & 55.2 & 42.4 & 40.6 & 76.1 & 127 & **19,628** & 1261 & 12,641 \\
**MPNTrackSeg**[47] & 73.7 & 68.8 & 58.6 & 80.6 & 207 & 25,036 & 1059 & 7233 \\
**GMPHD_MAF**[10] & 83.3 & 66.4 & 69.4 & 84.2 & 249 & 28,284 & 935 & 3985 \\
**EMNT**[46] & 83.7 & **77.0** & 70.0 & 84.1 & 234 & 27,943 & **666** & 4326 \\
**ReMOTS_[45] & **84.4** & 75.0 & **70.4** & 84.0 & 248 & 28,270 & 819 & 3999 \\
**Our Method** & **84.4** & 75.1 & 70.2 & **84.3** & **254** & 28,504 & 1043 & **3765** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison results for the MOTS test set. MOTS dataset.