# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 3 Results
### Ablation Study
#### 3.3.3 Affinity Matrix  
We compared the use of different affinity matrices for data association in our method, including the traditional IoU calculation, the DHIoU method we proposed, and the calculation with a dilatation coefficient of 1 in the DHIoU method. As shown in Table 5, our method demonstrates good performance across most metrics. While the introduction of the dilatation coefficient may result in slight decreases in some metrics, overall, it enhances  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline Constant & 55.6 & 90.3 & 56.0 & 78.7 & 39.5 \\ NSA & 56.6 & 90.2 & 56.1 & **79.8** & 40.3 \\ AMN & **58.0** & **90.4** & **57.3** & **79.8** & **42.3** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison results of different dynamic measurement noise methods in the DanceTrack validation set. The best results are shown in **bold**.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**CEF** & **AMN** & **DHIOU** & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline  & & & 46.8 & 88.3 & 51.6 & 70.5 & 31.2 \\ ✓ & & & 47.0 & 88.5 & 51.9 & 70.6 & 31.2 \\  & ✓ & & 56.9 & 90.3 & 56.3 & 79.7 & 40.8 \\  & ✓ & ✓ & 57.4 & 90.3 & 56.7 & 79.7 & 41.5 \\ ✓ & ✓ & & 55.6 & 90.2 & 56.2 & 79.1 & 39.2 \\ ✓ & & ✓ & 56.0 & 90.3 & 56.7 & 78.7 & 40.1 \\  & ✓ & ✓ & 57.5 & 90.2 & 57.0 & 79.6 & 41.7 \\ ✓ & ✓ & ✓ & **58.0** & **90.4** & **57.3** & **79.8** & **42.3** \\ \hline \hline \end{tabular}
\end{table}
Table 3: Ablation experiments on the DanceTrack validation set. The best results are shown in **bold**.  
the overall performance. The dilatation coefficient can effectively alleviate the issue of excessively small multiplicative results, thereby improving tracking performance, and it is also very straightforward to implement.  
Since our model performs associations not only on high-confidence detection boxes but also conducts a second association on low-confidence boxes, each association requires the calculation of an affinity matrix. Therefore, we compared three scenarios, both associations using IoU distance to calculate the affinity matrix, and the first match using our DHIoU method, while employing different methods for the second association. As shown in Table 6, when both associations utilize our DHIoU method, all metrics achieve the best results. These results highlight that using the DHIoU method significantly enhances data association and overall tracking effectiveness.  
We believe that, compared to the width state, the height state is more advantageous for object association. Similar to DHIoU, we propose Dilatation Width IoU (DWIoU), which calculates the IoU using width instead of height. To ensure a fair comparison, we set the dilatation coefficient \(d\) to 1.0. As shown in Table 7, the width state negatively impacts association performance, while the height state has a positive effect. We believe this is because changes in the height state typically occur between actions such as crouching, standing, and jumping, whose motion patterns are relatively predictable and continuous, allowing the Kalman filter to model them effectively. In contrast, changes in the width state usually occur between limb movements or posture changes, with more complex and unpredictable motion patterns that exhibit non-linear behavior, posing a significant challenge for Kalman filter modeling.