# Multi-Object Tracking with Predictive Information Fusion and Adaptive Measurement Noise
## 4 Discussion  
Our proposed method enhances tracking performance through three key aspects: improving the handling of low-confidence boxes, reducing the impact of occlusion and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**First** & **Second** & **HOTA** & **MOTA** & **IDF1** & **DetA** & **AssA (\%)** \\
**Association** & **Association** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **AssA (\%)** \\ \hline IoU & IoU & 55.6 & 90.1 & 56.2 & 79.1 & 39.2 \\ DHIoU & IoU & 57.7 & 90.2 & 56.8 & **79.8** & 41.9 \\ DHIoU & DHIoU & **58.0** & **90.4** & **57.3** & **79.8** & **42.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 6: A comparison of different methods for calculating the affinity matrix in two associations on the DanceTrack validation set. The best results are shown in **bold**. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline IoU & 55.6 & 90.1 & 56.2 & 79.1 & 39.2 \\ DWIoU (\(d=1\)) & 51.4 & 90.0 & 49.4 & 79.3 & 33.5 \\ DHIoU (\(d=1\)) & **56.7** & **90.4** & **56.3** & **80.0** & **40.4** \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The comparison of the affinity matrix obtained from the height state and the one obtained from the width state using the DanceTrack validation set. The best results are shown in **bold**. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **HOTA (\%)** & **MOTA (\%)** & **IDF1 (\%)** & **DetA (\%)** & **AssA (\%)** \\ \hline IoU & 55.6 & 90.1 & 56.2 & 79.1 & 39.2 \\ DHIoU (\(d=1\)) & 56.7 & **90.4** & 56.3 & 80.0 & 40.4 \\ DHIoU & **58.0** & **90.4** & **57.3** & 79.8 & **42.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison results of different affinity matrix methods in the DanceTrack validation set. The best results are shown in **bold**. overlap noise on the motion model, and optimizing the computation of the affinity matrix to enhance the association capability for objects with complex motion. These improvements work together to significantly boost the overall tracking effectiveness of the model. As shown in Figures 5 and 6, the tracking visualization results validate the effectiveness of our proposed improvements. The object distribution in the MOT20 dataset is very dense, with significant overlap and occlusion between objects, presenting a substantial challenge for tracking tasks. In this context, our method effectively detects and successfully tracks objects, demonstrating its superiority in handling overlapping and occluded scenarios. The visualization results for MOT20-04 illustrate that our method can cope with the challenging environment of numerous pedestrians on a busy nighttime street, where an object with ID 19 can still be successfully tracked despite being occluded by other tracked objects. Furthermore, the visualization results for MOT20-08 further confirm that our method performs exceptionally well in crowded situations. In the DanceTrack dataset, the objects exhibit complex motion patterns with frequent occlusions and overlaps, posing significant challenges for MOT. In such cases, our method can maintain stable tracking of objects over extended periods without loss, demonstrating its effectiveness. The visualization results for the DanceTrack-09 sequence show that, even when faced with the complex movements of dancing, we can achieve stable tracking over long durations; after 100 frames, our method still accurately tracks each object. Additionally, the visualization results for the DanceTrack-09 sequence further corroborate this point. Our proposed method performs excellently in handling object overlap in images, showing significant advantages over the baseline model. As shown in Figure 7, based on the ground truth labels provided by the DanceTrack validation set, we calculated the Intersection over Union (IoU) of object bounding boxes for each frame. When the overlap rate (IoU) exceeded 0.7, we counted and accumulated the number of objects in that frame.