## 4 Experiment
### Experiment Results
Specifically, the green dotted line and the multiple colors of other lines represent the baseline performance and each \(\tau^{amb}\), respectively. As illustrated in Figure 12, the results show the highest performance for \(\tau^{blur}\) with the value of 24 on MOTDT and for \(\tau^{blur}\) with 70 on DeepSORT. They indicate that MOTDT is  
Figure 11: The experimental results of the second experiment to find delicate parameters based on the parameters that performed the best in the first experiment. \(\tau^{related}\) and \(\tau^{amb}\) vary in units of 0.01 ranged over the value of the base parameter. Figure 12: Experimental results to evaluate the effect of SRGAN- or DeblurGAN-based image restoration modules on the appearance model of the baseline tracker. The experiment proceeds with increasing \(\tau^{blur}\) by 1 unit. effective in severely dynamic situations, but DeepSORT is effective in relatively weakly dynamic situations. The further experimental results represent the predominance of SRGAN in most cases. In fact, the images restored by DeblurGAN tend to make blur effects disappear more clearly. However, when we zoomed in on the image as shown in Figure 13, specific patterns that are newly created are observed. We speculate that this pattern has a negative effect on the appearance model. **Analysis of the face appearance model. ** We conduct an experiment with three distance measurements (Equations (11)-(13)) to find an optimal parameter for \(\tau^{facc}\) in the face appearance model. Figure 14 shows the effect of the face appearance model on the baseline tracker adjusting \(\tau^{facc}\) by 0.01 unit for the range from 0 to 1 for each distance measurement. As shown in Figure 14, MOTDT achieves the highest performance when its distance measurement is \(l_{last}\) and the value of \(\tau^{facc}\) is 0.65. This indicates that, because MOTDT has a relatively large number of tracking candidates, using the most recent face information contributes to the better performance rather than using all of the past face information. Moreover, when only the face information of the last state is used, the embedding vector distances are relatively close due to the rare changes of the face. The performance is thus improved at a relatively strong threshold. DeepSORT achieves the highest performance  
Figure 14: Experimental results to validate our proposed face appearance model. Specifically, the color of each line represents the corresponding distance measurement. Figure 13: An inferred image by DeblurGAN. We apply the learning method and dataset proposed in [37]. when the distance measurement is \(l_{mean}\) and the value of \(\tau^{face}\) is 0.58. Because DeepSORT relies on the detection results to configure tracking candidates, comparing the limited candidates with the past face information more intensively contributes to the better performance. In addition, since the face information of the entire past states is used, various embedding vectors should be considered, resulting in the improved performance at relatively weak thresholds. **Experiments on MOT challenge datasets. ** In order to check how effective the finished model is when applied to an unfamiliar environment, experiments are conducted in a new environment using parameters found through previous experiments. We use the MOT Challenge dataset for simulating a new environment. Among all MOT Challenge datasets, only four of those which are based on the moving platform (MOT16-(05,10,11,13)) are used to match the scope of our paper. The experimental results are reported in Table 3, and the arrows after evaluation metrics indicate that the higher (\(\uparrow\)) and lower (\(\downarrow\)) values represent the better performance. The experimental results show the performance improvement of the main metrics, MOTA and IDF1, in the datasets (except for one of the four datasets). The reason why performance has not improved in some datasets can be interpreted as a problem of excessively low image quality.