## 4 Experiment
### Experiment Results  
**Evaluation metric. ** We use Multiple Object Tracking Accuracy (MOTA) [43], ID F1 score [44], the ratio of Mostly Tracked targets (MT), the ratio of Mostly Lost targets (ML), and the number of ID Switches (IDS) as the performance metrics, which are significant among several metrics used in the MOT Challenge. Specifically, MOTA and IDF1 are considered to be the most important performance metrics. MOTA represents false positives, missed targets, and identity switches together, and IDF1 represents the consistent tracking rate of object ID. In our experiments, we consider the MOTA score as the first priority and the IDF1 score as the second priority in performance. For the evaluation, the MATLAB-based MOT Challenge Development Kit is used according to the MOT16 benchmark rule, and the three dataset benchmarking results are given with their weighted average scores. **Evaluation baseline tracker. ** The default \(\tau^{detect}\) value of the detector may provide biased performance to some trackers. Therefore, to avoid this, we first observe the performances of the selected detector's detect confidence threshold, \(\tau^{detect}\), and fix the threshold that results in the highest performance for the baseline tracker. In the experimental results for the baseline shown in Figure 9, the best values of \(\tau^{detect}\) are 0.31 for MOTDT and 0.40 for DeepSORT. We specify MOTA and IDF1 scores for the thresholds as the baseline performance to compare with our methods. Figure 8: Measured Laplacian variances of each dataset. The horizontal and vertical axis represents the dataset and the averaged value of the Laplacian variance for each dataset, respectively. Figure 9: MOTA and IDF1 scores varying \(\tau^{detect}\) for two baseline trackers, MOTDT and DeepSORT. Evaluation and ablation studies.The finalized model proposed in this paper is combined with three aforementioned methods. The ablation study confirms the effectiveness of the combination of each method. When combining methods, we set parameters that record the highest performance of each of the three methods without the additional hyperparameter settings for each combination. Table 2 shows the experimental results of the ablation study. The arrows after evaluation metrics indicate that the higher (\(\uparrow\)) and lower (\(\downarrow\)) values represent the better performance. In this table, specifically, the method column refers to which of the three methods--the re-detection, the site restoration, and the face appearance model (simplified as \"face appearance\")--are applied to the corresponding baseline tracker. The proposed models generally perform better than the baseline methods, even when using some of our methods, and the best when all methods are combined. Specifically, comparing our finalized model with the baseline, the MOTA score is improved by 0.65% in MOTDT, 0.57% in DeepSORT, and the IDF1 score by 0.6% in MOTDT and 1.79% in DeepSORT. **Analysis of re-detection. ** Two experiments are conducted to adjust the aforementioned thresholds, \(\tau^{amb}\) and \(\tau^{redetect}\), to find the optimal parameters for the re-detection method. In the first experiment, \(\tau^{amb}\) ranges from 0 to \(\tau^{detect}\), and \(\tau^{redetect}\) ranges from \(\tau^{detect}\)