# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 4 Results
### Ablation Study  
In order to prove the effectiveness of each module of the method proposed in this paper, ablation experiments were conducted on the MOT20 train. The baseline is the YOLOX [2] detector. IOU stands for using IOU to calculate matching, NMS represents the NMS post-module, UR represents Unsupervised pre-training for ReID, and BI represents Bicubic interpolation module. The research tests the performance of each method on five indicators including MOTA, IDF1, FP, FN, and IDs.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Mode** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** & **MT\(\uparrow\)** & **ML\(\downarrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** \\ \hline MOTR [32] & one-stage & 67.4 & 67.0 & 34.6 & 24.5 & 32,355 & 149,400 & 1992 \\ CorrTracker [8] & one-stage & 76.5 & 73.6 & 47.6 & 12.7 & 29,808 & 99,510 & 3369 \\ FairMOT [7] & one-stage & 73.7 & 72.3 & 43.2 & 17.3 & 27,507 & 117,477 & 3303 \\ CSTrack++ [6] & one-stage & 70.6 & 71.8 & 38.2 & 17.8 & - & - & 1071 \\ TrackFormer [33] & one-stage & 62.5 & 60.7 & - & - & 32,828 & 174,921 & 3917 \\ TransMOT [13] & two-stage & 76.7 & 75.1 & **51.0** & 16.4 & 36,231 & 125,665 & **1042** \\ MPNTrack [34] & two-stage & 58.8 & 61.7 & 28.8 & 33.5 & **17,413** & 213,594 & 1185 \\ Our method & two-stage & **78.3** & **76.1** & 50.8 & **11.8** & 33,754 & **93,797** & 1435 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison with the state-of-the-art methods over the dataset MOT17 test set. The best results are shown in **bold**. The next best result is underlined.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **Mode** & **MOTA\(\uparrow\)** & **IDF1\(\uparrow\)** & **MT\(\uparrow\)** & **ML\(\downarrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** \\ \hline CorrTracker [8] & one-stage & 65.2 & 69.1 & 66.4 & 8.9 & 79,429 & 95,855 & 5183 \\ FairMOT [7] & one-stage & 61.8 & 67.3 & 68.8 & **7.6** & 103,440 & 88,901 & 5243 \\ TransMOT [13] & two-stage & **77.5** & **75.2** & **70.7** & 9.1 & **34,201** & **80,788** & **1615** \\ Our method & two-stage & 75.7 & 71.4 & 68.3 & 9.7 & 42,134 & 90,833 & 2026 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison with the state-of-the-art methods over the dataset MOT20 test set. The best results are shown in **bold**. The next best result is underlined.  
As shown in Table 3, the experimental results verify the effectiveness of each module of the method. Comparing the first and second rows of the table, MOTA increased by 8.8%, IDF1 increased by 0.8%, and both FP and IDs decreased, which proved that NMS post-position played a role and improved the performance of MOT. Comparing the second and third rows of the table, MOTA is significantly improved by 0.3%, which proves that unsupervised pre-training can make the model have the ability to reduce the domain gap and improve the generalization ability of the model on the data set. Comparing the 3 rd and 4 th rows of the table, all indicators are improved, which proves the effectiveness of Bicubic interpolation.