# An Effectively Finite-Tailed Updating for Multiple Object Tracking in Crowd Scenes
## 4 Experiment
### Hyperparameter Comparison and Analysis Experiments  
How many frames should be selected? How should the features of these frames for updating be combined? How should the weight of each frame be allocated? In order to solve these problems, we conduct a lot of experiments to find an optimal update strategy to obtain the best appearance template. Comparison of different backbones: In order to verify the effect of our update method on which backbone network is better, we use different backbone networks, such as vanilla ResNet, Feature Pyramid Network, High-Resolution Network, and DLA-34. In order to ensure the fairness of the experiment, except for the different backbone networks, the other experimental conditions are consistent. For all backbone networks, we set the stride of the final feature map to be four. In order to make the vanilla ResNet's stride of the final feature map four, we add three upsampling operations. The result is shown in Table 2.  
Comparison of different backbones on the validation set of MOT17 dataset. Comparison of different Re-ID feature dimensions: It is shown in FairMOT [27] that feature dimensionality plays an important role in balancing detection and tracking accuracy. In order to verify which dimensional Re-ID features are most effective in our update method, we conduct a series of experiments on the Re-ID feature dimensions 512, 256, 128, and 64. To maintain the fairness of the experiment, the experimental conditions \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Tracker** & **MOTA\(\uparrow\) (\%)** & **IDF1\(\uparrow\) (\%)** & **HOTA\(\uparrow\)** & **MT\(\uparrow\) (\%)** & **ML\(\downarrow\) (\%)** \\ \hline \multirow{8}{*}{MOT16} & FairMOT [27] & 74.9 & 72.8 & - & 44.7 & 15.9 \\ \cline{2-7}  & TMOH [38] & 63.2 & 63.5 & 50.7 & 27.0 & 31.0 \\ \cline{2-7}  & mfi\_tst [39] & 59.9 & 58.7 & 46.9 & 24.1 & 30.8 \\ \cline{2-7}  & MPNTrack [40] & 58.6 & 61.7 & 48.9 & 27.3 & 34.0 \\ \cline{2-7}  & LPC\_MOT [41] & 58.8 & 67.6 & 51.7 & 27.3 & 35.0 \\ \cline{2-7}  & Tracktor++v2 [20] & 54.4 & 52.5 & 42.3 & 19.0 & 36.9 \\ \cline{2-7}  & Ours & 74.4 & 73.7 & 60.2 & 46.1 & 15.2 \\ \hline \multirow{8}{*}{MOT17} & FairMOT [27] & 73.7 & 72.3 & - & 43.2 & 17.3 \\ \cline{2-7}  & TMOH [38] & 62.1 & 62.8 & 50.4 & 26.9 & 31.4 \\ \cline{2-7}  & mfi\_tst [39] & 60.1 & 58.8 & 47.2 & 26.0 & 27.2 \\ \cline{2-7}  & MPNTrack [40] & 58.8 & 61.7 & 49.0 & 28.8 & 33.5 \\ \cline{2-7}  & LPC\_MOT [41] & 59.0 & 66.8 & 51.5 & 29.9 & 33.9 \\ \cline{2-7}  & Tracktor++v2 [20] & 56.3 & 55.1 & 44.8 & 21.1 & 35.3 \\ \cline{2-7}  & Ours & 73.1 & 73.0 & 59.7 & 45.0 & 16.8 \\ \hline \multirow{8}{*}{MOT20} & FairMOT [27] & 61.8 & 67.3 & - & 68.8 & 7.6 \\ \cline{2-7}  & TMOH [38] & 60.1 & 61.2 & 48.9 & 46.7 & 17.8 \\ \cline{1-1} \cline{2-7}  & mfi\_tst [39] & 59.3 & 59.1 & 47.1 & 41.1 & 17.3 \\ \cline{1-1} \cline{2-7}  & MPNTrack [40] & 57.6 & 59.1 & 46.8 & 38.2 & 22.5 \\ \cline{1-1} \cline{2-7}  & LPC\_MOT [41] & 56.3 & 62.5 & 49.0 & 40.9 & 17.8 \\ \cline{1-1} \cline{2-7}  & Tracktor++v2 [20] & 52.6 & 52.7 & 42.1 & 29.4 & 26.7 \\ \cline{1-1} \cline{2-7}  & Ours & 59.7 & 67.8 & 54.0 & 66.5 & 7.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the state-of-the-art methods on the Multiple Object Tracking Benchmark. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **MOTA\(\uparrow\) (\%)** & **IDF1\(\uparrow\) (\%)** & **IDs\(\downarrow\)** & **AP\(\uparrow\)** & **TPR\(\uparrow\)** \\ \hline ResNet-34 & 63.7 & 67.4 & 427 & 75.2 & 90.8 \\ \hline ResNet-50 & 63.8 & 67.9 & 506 & 75.8 & 92 \\ \hline ResNet-34-FPN & 64.5 & 70.2 & 367 & 77.9 & 94.3 \\ \hline HRNet-W18 & 67.6 & 74.5 & 317 & 80.7 & 94.8 \\ \hline DLA-34 & 69.0 & 73.5 & 304 & 81.4 & 94.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different backbones on the validation set of MOT17 dataset. are kept constant, except for the different dimensions of the Re-ID features.