# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 5 Conclusions  
In this work, we have proposed a novel appearance template update mechanism, named FTU, in the MOT field. By combining the accumulated appearance templates of the objects in multiple earlier frames with the Re-ID feature of the current frame, FTU succeeds in identifying the occluded objects even in scenes with dense crowds and occlusions. We have conducted extensive experiments on three widely used datasets, including the MOT16, MOT17, and MOT20 datasets. The experimental results have demonstrated the superiority of our proposed method over other state-of-the-art methods. In addition, we have conducted several analysis experiments to show the effectiveness of the proposed strategy as well as the parameter settings of our method.  
We believe that this method still has some room for improvement. For example, the current update window parameter is fixed for the entire video. We believe that adaptive update windows can further improve system performance. In the future, we will try to integrate this update strategy with new multi-object tracking algorithms and apply it to multi-modal tracking tasks.  
Conceptualization, B.X. and D.L.; methodology, B.X.; software, B.X.; validation, B.X., D.L. and L.L.; formal analysis, B.X.; investigation, B.X.; resources, M.Z.; data curation, B.X.; writing--original draft preparation, B.X.; writing--review and editing, R.Q.; visualization, B.X.; supervision, D.L.; project administration, D.L.; funding acquisition, D.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Tracker** & **MOTA\(\uparrow\) (\%)** & **IDFI\(\uparrow\) (\%)** & **HOTA\(\uparrow\)** & **MT\(\uparrow\) (\%)** & **ML\(\downarrow\) (\%)** \\ \hline \multirow{3}{*}{MOT16} & FairMOT [27] & 74.9 & 72.8 & - & 44.7 & 15.9 \\ \cline{2-7}  & Ours & 74.4 & 73.7 & 60.2 & 46.1 & 15.2 \\ \hline \multirow{3}{*}{MOT17} & FairMOT [27] & 73.7 & 72.3 & - & 43.2 & 17.3 \\ \cline{2-7}  & Ours & 73.1 & 73.0 & 59.7 & 45.0 & 16.8 \\ \hline \multirow{3}{*}{MOT20} & FairMOT [27] & 61.8 & 67.3 & - & 68.8 & 7.6 \\ \cline{2-7}  & Ours & 59.7 & 67.8 & 54.0 & 66.5 & 7.7 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Comparison of the baseline method on Multiple Object Tracking Benchmark.  
Figure 7: Comparison of our method and the baseline method. We compare the same video data of MOT16. The red arrow points to the object we used for the tracking-performance comparison. It can be seen from the figure that, when using the FairMOT method for tracking, the ID of the object changes from 363 to 217 after occlusion. However, when using our method for tracking, the ID remains at 366 after occlusion.  
**Funding:** This research was funded by AI+ Project of NUAA (XZA20003), National Science Foundation of China (61772268)  
**Institutional Review Board Statement:** Ethical review and approval were waived for this study, duo to the datasets are public datasets, and other paper such as JDE and FairMOT use the public datasets.  
**Informed Consent Statement:** Informed consent was obtained from all subjects involved in the study.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.