## 4 Experiment
### Experiment Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Baseline** & **Method** & **MOTA (\(\uparrow\))** & **IDF1 (\(\uparrow\))** & **MT (\(\uparrow\))** & **ML (\(\downarrow\))** & **ID sw (\(\downarrow\))** \\ \hline \multirow{6}{*}{MOTDT} & - & 32.513 & 43.931 & 40 & 120 & 162 \\ \cline{2-6}  & re-detection & 32.641 & 44.017 & 41 & 118 & 172 \\ \cline{2-6}  & site restoration & 32.818 & 44.263 & 41 & 120 & 163 \\ \cline{2-6}  & face appearance & 32.832 & 44.226 & 42 & 120 & **159** \\ \cline{2-6}  & re-detection & 32.959 & 44.355 & 42 & 118 & 173 \\ \cline{2-6}  & site restoration & 32.886 & 44.492 & 43 & 118 & 163 \\ \cline{2-6}  & site restoration & 33.118 & 44.419 & 43 & 120 & 160 \\ \cline{2-6}  & re-detection & **33.164** & **44.531** & **44** & **118** & 167 \\ \cline{2-6}  & face appearance & & & & & \\ \hline \multirow{6}{*}{DeepSORT} & - & 53.607 & 56.114 & 83 & 69 & 132 \\ \cline{2-6}  & re-detection & 53.929 & 57.616 & 84 & 67 & 137 \\ \cline{2-6}  & site restoration & 53.707 & 56.165 & 84 & 69 & 130 \\ \cline{2-6}  & face appearance & 53.834 & 56.471 & 83 & 68 & 123 \\ \cline{2-6}  & re-detection & 54.007 & 57.656 & 85 & 67 & 137 \\ \cline{2-6}  & re-detection & 54.093 & **57.943** & 85 & 67 & 134 \\ \cline{2-6}  & face appearance & & & & & \\ \hline \multirow{6}{*}{DeepSORT} & \multirow{2}{*}{53.989} & \multirow{2}{*}{56.709} & \multirow{2}{*}{84} & \multirow{2}{*}{68} & \multirow{2}{*}{**117**} \\  & face appearance & & & & & \\ \cline{1-1} \cline{2-6}  & site restoration & & & & & \\ \cline{1-1} \cline{2-6}  & site restoration & **54.180** & 57.906 & **85** & **67** & 130 \\ \cline{1-1} \cline{2-6}  & face appearance & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental results of ablation studies based on the baseline trackers, MOTDT and DeepSORT. Highlighting represents the best performance. to 0.9. Those parameters are adjusted in units of 0.05 to identify tendencies in performance. Delicate evaluation is conducted in the second experiment, adjusting those parameters to 0.01 units for the \(\pm\)0.05 range on the value which shows the maximum MOTA score in the first experiment and where the IDF1 score was above the baseline. Figures 10 and 11 represent the results of the first and second experiments, respectively, where the green dotted line represents the baseline performance. According to the results shown in Figure 11, the highest score is achieved when the value of \(\tau^{amb}\) and \(\tau^{redetect}\) are 0.25 and 0.61 in MOTDT and the value of \(\tau^{amb}\) and \(\tau^{redetect}\) are 0.3 and 0.64 in DeepSORT, respectively. The reason why DeepSORT is enhanced more than MOTDT when using re-detection is that DeepSORT uses only detect results as tracking linkage candidates. This indicates that DeepSORT is more dependent on detect results. On the contrary, MOTDT uses not only the detect results but the locations estimated by the motion model as an association candidate. Consequently, DeepSORT is more affected in the performance improvement by our re-detection method. **Analysis of site restoration. ** We perform an experiment to adjust the aforementioned threshold, \(\tau^{blur}\), to find the optimal parameter for the best configuration of the site restoration method. As an additional experiment, we compare SRGAN, which we use in the image restoration module, with DeblurGAN, a representative model based on GAN for the Deblur problem. DeblurGAN is trained on our the same dataset as SRGAN, and the detailed training configuration follows the default settings suggested in the paper. Figure 12 shows the effect of the image restoration module on the appearance model by adjusting the \(\tau^{blur}\) by 1 unit for the range from 0 to 200. Figure 10: The results of the first experiment varying \(\tau^{redetect}\) and \(\tau^{amb}\) in 0.05 units. The vertical and horizontal axes represent the MOTA and IDF1 score and \(\tau^{redetect}\), respectively.