## 3 Experiments
### Ablation Studies  
In order to prove the validity of each part of our algorithm, we disabled the functions of each part of the algorithm and evaluated them on the MOT15 training set. As shown in Table 2, the Proposed Algorithm is the result of the complete algorithm in our paper.  
As can be seen from the Table 2, each part of the algorithm has a certain impact on the whole algorithm. Among them, deep appearance features have the largest impact on MOTA, MT, and ML. The results support that DAFs are much more discriminative than traditional features. Trajectory states and state transitions also have a great influence. This method helps us greatly reduce the number of IDS, FP, and Frag, but at the same time it also adds a lot of FN. This is because the trajectory is not shown in the Tentative state, which causes the tracker to miss many supposedly correct trajectories, but also as a result of this the tracker can screen out many false detections. In fact, our matching strategy also depends on the designed trajectory states, which are also affected to some extent. Additionally, similarity distance measurements and diverse and hierarchical matching strategies also have certain impact on the metrics to different degrees.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Method** & **MOTA(\(\uparrow\))** & **MT(\(\uparrow\))** & **ML(\(\downarrow\))** & **FP(\(\downarrow\))** & **FN(\(\downarrow\))** & **IDS(\(\downarrow\))** & **Frag(\(\downarrow\))** \\ \hline Proposed Algorithm & 30.0 & 59 & 269 & 4101 & 23,580 & 269 & 845 \\ \hline B1 & 26.3 & 60 & 245 & 5088 & 23,028 & 1284 & 1518 \\ \hline B2 & 26.8 & 40 & 297 & 2559 & 25,891 & 742 & 889 \\ \hline B3 & 25.8 & 40 & 306 & 4018 & 24,126 & 933 & 968 \\ \hline B4 & 27.3 & 40 & 300 & 2488 & 26,133 & 404 & 690 \\ \hline \hline \end{tabular}  
* B1: We disabled all trajectory states and states transition proposed in Section 2.1, i.e., deleted trajectories that were not associated with detections directly in the current frame. B2: We disabled the trajectory confidence proposed in Section 2.2 and the matching strategy proposed in Section 2.5. We replaced the matching strategy between trajectories and detections with directly using the Hungarian algorithm according to the similarity distance in the current frame. B3: We disabled the deep appearance features proposed in Section 2.3 and replaced it with a histogram of oriented gradients (HOG) [31] appearance. B4: We disabled the similarity distance measurement proposed in Section 2.4 and replaced the correlation measure in the matching strategy with the measurement based on IoU.  
\end{table}
Table 2: Performance of our method when different components are disabled.  
In order to study the impact of numerous thresholds or weights on the algorithm, we have done a lot of comparative experiments. Since there are too many parameters involved, this paper does not list them one by one. Here, we take the threshold \(\theta_{TC}\) mentioned in Section 2.1 as an example, as shown in Table 3. From the table, we can see that the number of MT, FP, IDS, and Frag gradually decreased while ML and FN gradually increased with the increase of threshold. This also proves that our algorithm misses the correct trajectory while eliminating the error detection. The number of MOTA first increased and then decreased, reaching its maximum at 2, so we let \(\theta_{TC}=2\) in the following experiments. Similarly, we got the best value of other threshold parameters on the MOT15 benchmark dataset through experimental data, including \(\theta_{VD}=100\), \(\theta_{app}=0.2\), \(\theta_{mot}=9.4877\), \(\rho_{\text{K}}=0.03375\), \(\rho_{\text{D}}=25\), \(G^{i}=110\), where the value \(\theta_{mot}\) were obtained from the table of critical values of chi-square distribution.