## 4 Experiments
### Comparison with State-of-the-Art Methods  
In this section, we compare the performance of REACTrack with previous MOT methods on MOT16. As shown in Table 2, REACTrack achieves the best results on a variety of metrics and outperforms the comparable methods compared by a wide margin, especially on the MOTA, HOTA, IDF1 and IDS metrics. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**Baseline** & **PDDL** & **EFGCN** & **ACM** & **MOTA \(\uparrow\)** & **IDF1 \(\uparrow\)** & **MT \(\uparrow\)** & **ML \(\downarrow\)** & **FP \(\downarrow\)** & **FN \(\downarrow\)** & **IDS \(\downarrow\)** \\ \hline \(\surd\) & & & & 71.7 & 75.4 & 33.2 & 24 & 923 & 7934 & 947 \\ \(\surd\) & \(\surd\) & & & 73.9 & 78.2 & 35.9 & 20.3 & 847 & 7713 & 495 \\ \(\surd\) & \(\surd\) & \(\surd\) & & 77.2 & 80.3 & 37.1 & 17.8 & 756 & 7535 & 457 \\ \(\surd\) & \(\surd\) & \(\surd\) & \(\surd\) & **81.1** & **82.4** & **49.5** & **14.2** & **672** & **6952** & **292** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: REACTrack ablation experiments. \(\surd\) denotes addition of this module to the base line, which is FairMOT. PDDL, EFGCN, and ACM denote the Predictive Distance Difference Loss, Effective Feature Graph Convolutional Neural Network, and Association Correction modules, respectively. The best results are shown in bold. In order for our algorithm to be compared with ByteTrack, it will be based on FairMOT as shown in Table 3. In order to evaluate the effectiveness of EFGCN Re-ID and ACM, we compare it with ByteTrack using different patterns. The first row represents the tracking process embedded by FairMOT combined with Re-ID. The second row indicates the combination of FairMOT with our proposed EFGCN Re-ID. The MOTA is improved by 5.1% and the IDF1 is improved by 2.5%, so the features extracted by EFGCN are more effective, and it also reduces the dependence on the detection performance and improves the tracking effectiveness. The fourth row compares our ACM algorithm with BYTE. The MOTA improves from 70.3 to 73.4 and the IDF1 improves from 73.2 to 74.8, indicating that our proposed method improves the tracking ability because of the embedding of the EFGCN Re-ID in the correlation process as well as the in-depth processing judgments of the ACM for the occluded targets. The IDS decreases from 236 to 221, which indicates better robustness of tracking. As shown in Table 4, we have compared the performance of REACTrack with the previous MOT method on MOT17. In the one-shot method approach, REACTrack improved the IDF1 metric of FairMOT from 72.3 to 80.1, improves MOTA from 73.7 to 79.2, improves HOTA from 59.3 to 62.8, and reduces the IDs from 3303 to 2031. Compared with the BYTE, we can see that Simple Track improves IDF1 from 77.3 to 80.1 and reduces ID from 2196 to 2031, and the HOTA of both methods is also close. This shows that our tracking method is more effective than the JDE strategy, and the detector used does not want to pass compared to the BYTE strategy, and it also shows that our proposed prediction of the distance difference loss is effective for MOTA, and also the reduction of the ID value shows that our proposed EFGCN and trajectory correlation method can can improve the quality of the features, and reduces the number of switching of the IDs in the case of target occlusion.