## 4 Experiments
### Results
#### 4.3.3 Detection-Based MOT Pipeline
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Gap**} & \multirow{2}{*}{**Tracking Method**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{3-11}  & & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** & **\% MT\(\uparrow\)** & **\% ML\(\downarrow\)** & **FM\(\downarrow\)** & **FPS\(\uparrow\)** \\ \hline \multirow{9}{*}{1} & SORT (\(IoU\)) & 84.92 & **88.89** & 13,514 & **47** & 2247 & 97 & 48.3 & 9.2 & 178 & **1444** \\  & SORT (\(A\)) & **86.43** & 88.20 & 13,819 & 113 & 2012 & **27** & **58.2** & 8.3 & 160 & 1375 \\  & SORT (\(W_{\text{M}}\)) & 86.11 & 88.43 & 13,732 & 77 & 2073 & 53 & 53.2 & 8.9 & 154 & 1426 \\  & Deep-SORT (\(IoU\)) & 81.28 & 86.02 & 13,893 & 1003 & 1761 & 204 & 48.9 & 5.8 & 128 & 155 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & 82.02 & 85.56 & 14,077 & 1070 & 1590 & 191 & 55.7 & **4.0** & 111 & 158 \\  & Deep-SORT (\(A\)) & 82.49 & 85.61 & **14,109** & 1027 & **1587** & 162 & 57.2 & 4.6 & **103** & 158 \\ \hline \multirow{9}{*}{2} & SORT (\(IoU\)) & 79.73 & **87.75** & 8718 & **53** & 2022 & 128 & 35.8 & 12.3 & 151 & 1407 \\  & SORT (\(A\)) & **83.37** & 86.09 & 9239 & 178 & 1593 & **36** & 49.4 & 8.6 & 130 & 1413 \\  & SORT (\(W_{\text{M}}\)) & 83.08 & 86.61 & 9117 & 88 & 1674 & _77_ & 43.5 & 9.0 & 127 & **1480** \\  & Deep-SORT (\(IoU\)) & 75.28 & 84.16 & 8975 & 794 & 1697 & 196 & 38.0 & 8.3 & 126 & 152 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & 78.65 & 82.86 & 9414 & 866 & 1317 & 137 & 51.2 & **4.0** & 89 & 153 \\  & Deep-SORT (\(A\)) & 79.41 & 82.98 & **9470** & 840 & **1279** & 119 & **51.9** & 4.9 & **83** & 153 \\ \hline \multirow{9}{*}{3} & SORT (\(IoU\))Table 7 presents the detection-based MOT pipeline results achieved on the ISR500 sub-dataset. Once again, the performance of the YOLOv3 is crucial for a promising object tracking performance. Overall, similar to the results attained on the ISR200 sub-dataset, the SORT method obtained the best results. However, the Deep-SORT method reached the best values on the FN, MT, and ML evaluation metrics, showing that the Deep-SORT could be most suitable for tracking larger object sequences. This happens due to an increased capability of the Deep-SORT method to re-identifying lost object sequences compared with the SORT method, which struggles to predict the position of the object when the track starts to miss. As observed in the previous evaluations, the \(A\) cost matrix, in both SORT and Deep-SORT, achieved the best MT result, meaning that the object sequence is, at least, tracked in 80% of its life span, which is very important to successfully perform mobile robot navigation tasks. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**YOLO**} & \multirow{2}{*}{**Tracking Method**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{3-13}  & & **\% MOTA\(\uparrow\)** & **\% MOTP\(\uparrow\)** & **TP\(\uparrow\)** & **FP\(\downarrow\)** & **FN\(\downarrow\)** & **IDs\(\downarrow\)** & **\% MT\(\uparrow\)** & **\% ML\(\downarrow\)** & **FM\(\downarrow\)** & **FPS\(\uparrow\)** \\ \hline \multirow{6}{*}{\(Y_{M4}\)} & SORT (\(IoU\)) & **73.86** & 83.41 & 14,446 & **2779** & 1141 & 209 & 64.7 & 5.0 & 84 & **51** \\  & SORT (\(A\)) & 73.75 & 83.39 & 14,455 & 2806 & **1132** & 209 & **65.9** & **4.7** & 86 & **51** \\  & SORT (\(W_{M}\)) & 73.83 & **83.42** & **14,456** & 2794 & **1132** & **208** & 65.1 & 5.0 & **80** & **51** \\  & Deep-SORT (\(IoU\)) & 65.86 & 81.64 & 14,287 & 3884 & 1205 & 304 & 60.5 & 5.8 & 104 & 31 \\  & Deep-SORT (\(E_{D}^{IoU}\)) & 64.32 & 81.62 & 14,392 & 4232 & 1133 & 271 & 64.3 & 5.8 & 90 & 31 \\  & Deep-SORT (\(A\)) & 64.69 & 81.61 & 14,404 & 4186 & 1133 & 259 & 65.5 & 5.4 & 90 & 31 \\ \hline \hline \end{tabular}  
* The bold value highlights the best value on each column (in this case, each MOT evaluation metric).  
\end{table}