## 4 Experiments
### Results
#### 4.3.3 Detection-Based MOT Pipeline  
To evaluate the performance of the SORT and Deep-SORT object tracking methods in real scenarios, an evaluation using the YOLOv3 object detector algorithm feeding the tracking methods was performed. Moreover, to also evaluate the influence that the object detector performance may have over the object tracking performance, four YOLOv3 models with different performances were used. The four YOLOv3 models were trained on the same data (ISR RGB-D Dataset), and on the same conditions, varying only the number of training epochs. Each used YOLOv3 model has the following mean average precision: \(Y_{M1,\dots,M4}=\{38\%,60\%,80\%,90\%\}\). Table 6 presents the detection-based MOT pipeline results achieved on the ISR200 sub-dataset. As expected, the YOLOv3's performance had a significant role in the overall pipeline. As the YOLOv3 performance increased, the object tracking performance also increased. In the case of a poor YOLOv3's performance, the number of FN was so high, especially on the Deep-SORT method, which achieved a negative accuracy (MOTA). Regardless of the YOLOv3's performance, in these conditions, SORT outperformed the Deep-SORT method. Moreover, the three data association cost matrices used on the SORT method reached similar results, being the default \(IoU\) cost matrix able to achieve the best MOTA and FP results, while the \(A\) data association metric got the best MT values. Note that using \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Cost Matrix**} & \multicolumn{8}{c}{**Evaluation Metrics**} \\ \cline{2-10}  & \% MOTA\(\uparrow\) & \% MOTP\(\uparrow\) & TP\(\uparrow\) & FP\(\downarrow\) & FN\(\downarrow\) & IDs\(\downarrow\) & \% MT\(\uparrow\) & \% ML\(\downarrow\) & FM\(\downarrow\) & FPS\(\uparrow\) \\ \hline \multicolumn{10}{c}{Deep-SORT with Class Gate Metric} \\ \hline \(IoU\) & 90.80 & 89.66 & 30,989 & 1357 & 1447 & 199 & 72.3 & **0.3** & 142 & 163 \\ \(D_{E}\) & 91.09 & 89.53 & 31,100 & 1372 & 1367 & 168 & 76.3 & **0.3** & 131 & 167 \\ \(R\) & 89.12 & **90.27** & 30,467 & 1384 & 1783 & 385 & 62.3 & **0.3** & 292 & 166 \\ \(E_{IoU}^{IoU}\) & 91.15 & 89.52 & **31,124** & 1376 & **1350** & **161** & 78.4 & **0.3** & 130 & 165 \\ \(R^{D_{E}}\) & 90.90 & 89.86 & 30,994 & **1328** & 1401 & 240 & 75.7 & **0.3** & 160 & 166 \\ \(R^{D_{E}}\) & 91.07 & 89.54 & 31,087 & 1367 & 1381 & 167 & 76.3 & 0.6 & 134 & **169** \\ \(M\) & 91.15 & 89.55 & 31,116 & 1370 & 1354 & 165 & 77.8 & 0.6 & **125** & 163 \\ \(A\) & **91.23** & 89.55 & 31,123 & 1350 & **1350** & 162 & **78.7** & **0.3** & 126 & 168 \\ \(W_{M}\) & 91.09 & 89.56 & 31,103 & 1376 & 1363 & 169 & 76.6 & **0.3** & 119 & 166 \\ \hline \hline \end{tabular} The bold value highlights the best value on each column (in this case, each MOT evaluation metric).  
\end{table} 
Table 4: _Cont._an object detector may introduce additional errors to the object tracking pipeline, such as incorrect detections, shifted detections, miss detections, and wrong object classification. This can be observed by the obtained values of TP, FP, FN, and IDs, which directly influence the remaining evaluation metrics. As shown in Table 6, the object tracking performance increases as the YOLOv3's performance also increases, due to a large decrease in the FP values as well as the IDs values, which occurs due to an improvement of the object detection performance. Regarding the frame rate results, as expected, the SORT was faster than Deep-SORT since SORT does not have to extract visual features through a CNN.