## 4 Experiment
### Experiment Results
A dataset with improved performance (MOT16-(10,11,13)) has a high resolution of 1920 \(\times\) 1080 and has little noise, whereas a dataset without improved performance (MOT16-05) has a low resolution of 640 \(\times\) 480 and a lot of noise. Since low resolution and a lot of noise cause reasoning failure of the Image Restoration Module and Face Embedding Module, the threshold value used for verification needs to be strict. Therefore, when the image quality is significantly different from the experimental environment, retuning the \(\tau^{redirect}\), \(\tau^{blue}\), and \(\tau^{face}\) values to strict values may be advantageous for improving performance. From the overall experimental result, it is validated that our proposed methodology enhances the tracking performance when applied to a general situation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Baseline** & **Test Data** & **Method** & **MOTA (\(\uparrow\))** & **IDF1 (\(\uparrow\))** & **MT (\(\uparrow\))** & **ML (\(\downarrow\))** & **ID sw (\(\downarrow\))** \\ \hline \multirow{8}{*}{MOTDT} & MOT16-05 & - & 36.037 & **47.269** & 9 & 51 & **53** \\ \cline{2-8}  & \multirow{2}{*}{MOT16-10} & ours & **36.066** & 46.419 & 9 & 51 & 57 \\ \cline{2-8}  & \multirow{2}{*}{MOT16-11} & - & 13.492 & 22.463 & 3 & 40 & **46** \\ \cline{2-8}  & \multirow{2}{*}{MOT16-11} & ours & **13.785** & **23.129** & **4** & **38** & 48 \\ \cline{2-8}  & \multirow{2}{*}{MOT16-11} & - & 39.481 & 42.308 & 5 & 39 & **31** \\ \cline{2-8}  & \multirow{2}{*}{MOT16-13} & ours & **39.863** & **42.91** & 5 & **38** & 34 \\ \cline{2-8}  & \multirow{2}{*}{MOT16-13} & - & 3.7642 & 11.057 & 0 & 94 & 14 \\ \cline{2-8}  & \multirow{2}{*}{MOT16-13} & ours & **3.8253** & **11.188** & 0 & **93** & **13** \\ \hline \multirow{8}{*}{DeepSORT} & MOT16-05 & - & **38.09** & 45.215 & 13 & 53 & 41 \\ \cline{2-8}  & \multirow{2}{*}{MOT16-10} & ours & 37.87 & **45.293** & 13 & 53 & 41 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{MOT16-10} & - & 7.9477 & 17.072 & 1 & 40 & **34** \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{MOT16-11} & ours & **8.1507** & **18.532** & 1 & 40 & 35 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{MOT16-11} & - & 34.794 & 32.311 & 7 & 39 & 67 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{ours} & **34.848** & **32.968** & **8** & 39 & **66** \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{MOT16-13} & - & 2.5328 & 7.8663 & 0 & 97 & 8 \\ \cline{1-1} \cline{2-8}  & \multirow{2}{*}{ours} & **2.5764** & **8.3659** & 0 & 97 & **7** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results to confirm the effect of the proposed model applied to an unfamiliar environment. The parameters found through the previous experiment are used without change, and the MOT Challenge dataset is used as a test set. Highlighting represents better performance. **Qualitative results. ** Several qualitative assessments are conducted, summarizing some of the benchmark results. DeepSORT is selected for evaluation as the baseline tracker, and Figure 15 represents three qualitative evaluation results. The top row of each assessment is the benchmark result of the baseline tracker, and the bottom one is that of our proposed model on the baseline tracker. The bounding boxes with the same color in each evaluation result are meant to be recognized as the same objects by the tracker. In particular, the red bounding boxes represent the object which shows the largest difference between the proposed model and the baseline tracker. The examples clearly show that the three methods we propose are capable of maintaining the IDs of objects properly by overcoming the visual changes and the congestion of objects that occur in dynamic situations. 1. shows the selected frames in the Patrol dataset. This dataset causes severe motion blur, especially for distant objects due to the camera rotation. In the case of the baseline, the detection of the object fails, which is marked in red, and the tracking is terminated.