# IOF-Tracker: A Two-Stage Multiple Targets Tracking Method Using Spatial-Temporal Fusion Algorithm
## 3 Experimental Results and Analysis
### Ablation Study  
Our ablation study primarily aims to verify whether our R-IoU and optical flow feature fusion model can improve the benchmark metrics for multi-object tracking. Due to the official restrictions imposed by MOTChallenge on the number of attempts, researchers can submit results to the test server, we continued to use part of the MOT17-SDP training set for testing.  
As shown in Table 2, adding optical flow features alone to the baseline significantly reduces the benchmark tracking metrics. This issue arises because the MOT17 dataset is a multi-object tracking dataset with a high pedestrian density. The interactions between pedestrians during overlapping have a large impact on the optical flow field. Therefore, the tracking methods that rely solely on optical flow perform poorly. On the other hand, only using R-IoU features considers the spatial position information of bounding boxes, but unable to effectively handle partial occlusion scenarios. When a target is partially occluded, the R-IoU value may drop significantly, leading to incorrect matches or tracking loss. However, combining R-IoU and optical flow features can address or mitigate some of the above issues to some extent. Optical flow provides rich information about the target's motion, and even in the case of partial occlusion. Because optical flow can still be inferred effectively based on the motion patterns in the unoccluded regions. It handles situations where the target is partially occluded, thereby reducing incorrect matches or tracking loss. By fusing these two features, the robustness and accuracy of tracking can be improved. Additionally, the data in Table 2 also shows that the baseline with the fusion of R-IoU and optical flow features can achieve excellent scores in IDF1 and IDs.  
The ablation experiment in Table 3, which involves adding the R-IoU and optical flow feature fusion model in stages, shows that solely integrating our module into either the first or second stage of the two-stage tracker does not achieve the most ideal results. Only by using the R-IoU spatial features and optical flow temporal features in both stages can the test benchmark metrics reach relatively better results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **R-IoU** & **Opt-Flow** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDs \(\downarrow\)** \\ \hline \multirow{3}{*}{Baseline} & ✓ & & 91.147 & 84.693 & 339 \\  & & ✓ & 68.384 & 63.784 & 3446 \\  & ✓ & ✓ & **91.404** & **85.744** & **320** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Impact of the addition of the R-IoU and optical flow feature fusion model on the overall tracking algorithm performance (\(\uparrow\): The higher the parameter, the better. \(\downarrow\): The lower the parameter, the better. \(\downarrow\): indicates selection. Bold font: indicates the best value in that column).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Stage 1** & **Stage 2** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **IDs \(\downarrow\)** \\ \hline \multirow{3}{*}{Baseline + Fusion model} & ✓ & **91.425** & 85.557 & 329 \\  & ✓ & 91.368 & 84.513 & 360 \\ \cline{1-1}  & ✓ & ✓ & 91.404 & **85.744** & **320** \\ \hline \hline \end{tabular}
\end{table}
Table 3: The impact of applying the R-IoU and optical flow feature fusion model at different stages of the tracking framework on the overall tracking algorithm performance (\(\uparrow\): The higher the parameter, the better. \(\downarrow\): The lower the parameter, the better. \(\downarrow\): The lower the parameter, the better. \(\sigma\): indicates selection. Bold font: indicates the best value in that column).