# Single-Task Joint Learning Model for an Online Multi-Object Tracking Framework
## 3 Experiments
### Evaluation Multiple Object Tracking Performance
In both scenarios, the CD approach resulted in higher FP and IDs, leading to lower MOTA. The use of detection confidence and tracklet confidence reduced FP and IDs, slightly improving MOTA. Combining detection and tracklet confidence significantly reduced FP and IDs, with a slight increase in FN, ultimately achieving the best MOTA compared with the other methods. In summary, the different initialization methods exhibited consistent trends in both the scenarios. Combining detection and tracklet confidence proved effective in reducing FP and IDs while maintaining a balanced FP and FN, resulting in the highest MOTA. Experiments were performed on three videos: PETS09-S2L1, PETS09-S2L2, and TUD-Crossing to juxtapose the proposed method with leading-edge state-of-the-art multiple  
Figure 8: Impact of distance metrics on tracking results: (**a**) FP, (**b**) FN, (**c**) IDs, (**d**) MOTA. object-tracking techniques, as shown in Table 3. Except for TSML_CDE and CDT, which are offline tracking methods, all the others are online tracking methods. Deep-SORT, AP_HWDPL, CDA-DDAL, and KCF are among the tracking methods that utilize deep learning networks. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multirow{2}{*}{**Method**} & \multicolumn{8}{c}{**Metric**} \\ \cline{3-10}  & & **MT** & **ML** & **FP** & **FN** & **IDs** & **MOTA** & **MOTP** \\ \hline \multirow{5}{*}{PETS09-S2L1} & Deep-SORT [12] & 100\% & 0\% & 260 & 294 & 18 & 87.2\% & 77.6\% \\  & OMVT\_TAAC [48] & x & x & 372 & 592 & 68 & 76.9\% & 72.3\% \\  & DMOT\_MDP [49] & 90.4\% & x & x & x & 5 & 88.1\% & 68.7\% \\  & HMOT [50] & 94.7\% & 0\% & 121 & 318 & 20 & 89.6\% & 87.6\% \\  & Ours & 100\% & 0\% & 217 & 232 & 11 & 89.7\% & 77.4\% \\ \hline \multirow{5}{*}{PETS09-S2L2} & EAMTT [36] & 21.4\% & 4.8\% & 489 & 3146 & 189 & 60.3\% & 74\% \\  & AP\_HWDPL [15] & 14.3\% & 4.8\% & 387 & 3636 & 368 & 56\% & 74\% \\  & CDT [37] & 21.4\% & 19\% & 502 & 4429 & 113 & 47.7\% & 70.4\% \\  & KCF [40] & 16.7\% & 2.4\% & 1045 & 3418 & 181 & 51.8\% & 68.8\% \\  & MDP\_SubCNN [38] & 7.1\% & 9.5\% & 341 & 4524 & 196 & 47.5\% & 72.6\% \\  & CDA-DDAL [10] & 16.7\% & 4.8\% & 695 & 3075 & 193 & 50.8\% & 70.3\% \\  & Ours & 28.6\% & 4.8\% & 625 & 2903 & 367 & 62.2\% & 73\% \\ \hline \multirow{5}{*}{TUD-Crossing} & CDT [37] & 61.5\% & 0\% & 73 & 229 & 29 & 70\% & 73.3\% \\  & KCF [40] & 46.2\% & 0\% & 8 & 248 & 8 & 76\% & 75.1\% \\  & MDP\_SubCNN [38] & 69.2\% & 0\% & 32 & 195 & 6 & 78.9\% & 76.7\% \\  & TSML\_CDE [39] & 53.8\% & 7.7\% & 18 & 239 & 11 & 75.7\% & 75.8\% \\  & AP\_HWDPL [15] & 53.8\% & 0\% & 2 & 225 & 16 & 77.9\% & 75.8\% \\  & CDA-DDAL [10] & 61.5\% & 0\% & 39 & 181 & 12 & 78.9\% & 72.2\% \\  & Ours & 61.5\% & 0\% & 59 & 137 & 18 & 80.6\% & 72.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of multi-object tracking results between state-of-the-art methods and our method. Figure 9: Impact of initialization methods on tracking results, TUD-Crossing: **(a)** FP and FN, **(b)** IDs and MOTA; PETS09-S2L1 **(c)** FP and FN, **(d)** IDs and MOTA. In the experimental evaluation across three videos, namely PETS09-S2L1, PETS09-S2L2, and TUD-Crossing, our proposed method consistently outperformed the other state-of-the-art techniques. It excelled in metrics such as MT, ML, FN, and MOTA. The tracking results of the three videos are shown in Table 3. For PETS09-S2L1, our method achieved superior results, surpassing Deep-SORT by 2.5%, OMVT_TAAC by 12.8%, DMOT_MDP by 1.6%, and HMOT by 0.1% in terms of MOTA. For PETS09-S2L2, our method demonstrated excellent performance in MT, FN, and MOTA, outperforming EAMTT by 1.8%, AP_HWDPL by 6.1%, CDT by 14.4%, KCF by 10.3%, MDP_SubCNN by 14.6%, and CDA-DDAL by 11.3%. In the case of TUD-Crossing, our method outperformed ML, FN, and MOTA. In terms of MOTA, it outperformed CDT by 10.6%, KCF by 4.6%, MDP_SubCNN by 1.7%, TSML_CDE by 4.9%, AP_HWDPL by 2.7%, and CDA-DDAL by 1.7%.