## 3 Experiments
### Evaluations on MOT15  
To verify the efficiency of our method, as shown in Table 4, we exhibit part of the experimental results, where our method is denoted by HMB_DAF (Hierarchical-Matching-Based MOT with Deep Appearance Features). All the results in Table 4 were generated using public detection sets of the MOT15 benchmark as an input. In order to make a fair and effective comparison, we gathered some online methods published in recent years as the baseline methods, which also attach great importance to data association and make full use of appearance features. However, since many works did not publish the FPS of their methods as well as non-open sources, we only choose those methods which can be accessed publicly for fair comparison. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & **Method** & **MOTA(\(\uparrow\))** & **MT(\(\uparrow\))** & **ML(\(\downarrow\))** & **FP(\(\downarrow\))** & **FN(\(\downarrow\))** & **IDS(\(\downarrow\))** & **Frag(\(\downarrow\))** & **FPS(\(\uparrow\))** \\ \hline \multirow{4}{*}{A} & GMPHD [(32)] & 18.5\% & 3.9\% & 55.3\% & 7864 & 41,766 & 459 & 1266 & 19.8 \\ \cline{2-10}  & TSDA\_OAL [(33)] & 18.6\% & 9.4\% & 42.3\% & 16,350 & 32,853 & 806 & 1544 & 19.7 \\ \cline{2-10}  & MTSTracker [(34)] & 20.6\% & 9.0\% & 36.9\% & 15,161 & 32,212 & 1387 & 2357 & 19.3 \\ \cline{2-10}  & EAMTTpub [(35)] & 22.3\% & 5.4\% & 52.7\% & 7924 & 38,982 & 833 & 1485 & 12.2 \\ \cline{2-10}  & MDP [(36)] & 30.3\% & 13.0\% & 38.4\% & 9717 & 32,422 & 680 & 1500 & 1.1 \\ \hline \multirow{4}{*}{B} & TC\_SIAMESE [(37)] & 20.2\% & 2.6\% & 67.5\% & 6127 & 42,596 & 294 & 825 & 13.0 \\ \cline{2-10}  & INARLA [(10)] & 34.7\% & 12.5\% & 30.0\% & 9855 & 29,158 & 1112 & 2848 & 2.6 \\ \cline{2-10}  & AMIR15 [(9)] & 37.6\% & 15.8\% & 26.8\% & 7933 & 29,397 & 1026 & 2024 & 1.0 \\ \cline{2-10}  & Trackor++ [(38)] & 44.1\% & 18.0\% & 26.2\% & 6477 & 26,577 & 1318 & 1790 & 0.9 \\ \cline{2-10}  & HMB\_DAF (Ours) & 22.4\% & 3.3\% & 57.4\% & 5603 & 41,410 & 634 & 1686 & 37.6 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Tracking performance on the MOT15 dataset. Best results in each category appear in bold. (A) Traditional method, (B) deep learning method. Our method is denoted by HMB_DAF. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \(\theta_{TC}\) & **MOTA(\(\uparrow\))** & **MT(\(\uparrow\))** & **ML(\(\downarrow\))** & **FP(\(\downarrow\))** & **FN(\(\downarrow\))** & **IDS(\(\downarrow\))** & **Frag(\(\downarrow\))** \\ \hline
1 & 29.2\% & 67 & 250 & 5036 & 22,875 & 360 & 985 \\ \hline
2 & 30.0\% & 59 & 269 & 4101 & 23,580 & 269 & 845 \\ \hline
3 & 29.9\% & 57 & 280 & 3550 & 24,205 & 207 & 726 \\ \hline
4 & 29.5\% & 55 & 287 & 3185 & 24,785 & 177 & 665 \\ \hline
5 & 29.2\% & 46 & 302 & 2833 & 25,270 & 153 & 609 \\ \hline
8 & 27.8\% & 42 & 326 & 2147 & 26,552 & 112 & 509 \\ \hline
10 & 26.8\% & 33 & 346 & 1837 & 27,282 & 104 & 464 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of our method under different \(\theta_{TC}\). As can be seen in Table 4, although our method cannot outperform AMIR15 or MDP in terms of MOTA, we surpassed most algorithms in speed. Moreover, our FP was the lowest, which proves the effectiveness of our algorithm in handling error detection. However, as mentioned in Section 3.4, our FN was relatively high, leading to the low MT rate, but overall it had little effect on the comprehensive metric. Compared with the traditional MOT methods, our MOTA was higher except for MDP, which utilized machine learning to reinforcement its data association part, leading to low FPS. Compared with deep learning methods, our advantage mainly lies in FPS, as a result of their multiple complex network structures in each part of methods. For example, AMIR15 [9] designed three RNNs for object appearance, motion, and interaction separately in order to calculate the similarity between detections and objects. In contrast, we only used deep learning in feature extraction, which was a trade-off between accuracy and speed.