## 4 Experiments
### Results
#### 4.3.1 SORT and Deep-SORT Evaluation  
The proposed \(W_{M}\) data association cost matrix formulation requires the selection of three constant values (weights) to control the influence of each data association cost matrix. Table 2 shows the evaluation performed on the MOT17 dataset with different weight value combinations of the \(W_{M}\) cost matrix. Based on the achieved results, the highest MOTA value was attained using: \(\lambda_{\mathit{IoU}}=\frac{7}{10}\), \(\lambda_{\mathit{D_{E}}}=\frac{2}{10}\) and \(\lambda_{R}=\frac{1}{10}\). The aforementioned weight configuration has the minimum number of FP and IDs, despite the higher number of FNs. Furthermore, it has the minimum number of FM by a large amount. Therefore, throughout the following evaluations, the aforementioned values were used for the \(W_{M}\) cost matrix. Table 3 shows the results achieved on the MOT17 dataset. Regarding the SORT's results, the highest MOTA result was obtained using the default \(\mathit{IoU}\) cost matrix, being a similar result achieved by the \(E_{\mathit{D}}^{\mathit{IoU}}\), \(\mathit{R}^{\mathit{IoU}}\), \(\mathit{M}\), and \(W_{M}\) cost matrices. However, on the remaining evaluation metrics, the default IoU cost matrix was outperformed by the proposed cost matrices. The \(\mathit{M}\) cost matrix had the lowest number of FP, IDs, and FM, which represents the most accurate tracking for sequences generated by the SORT. The \(\mathit{A}\) cost matrix had the highest number of TP and the lowest number of FN, which is proportional to the percentage of MT sequences. For this work, which has in view mobile robot navigation tasks, those metrics could impact performance, as it can ensure that the object is successfully tracked until the object leaves the scene. Regarding the Deep-SORT \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multicolumn{4}{c}{**Weights**} & \multicolumn{10}{c}{**Evaluation Metrics**} \\ \hline \(\lambda_{\mathit{IoU}}\) & \(\lambda_{\mathit{D_{E}}}\) & \(\lambda_{R}\) & \% MOTA\(\uparrow\) & \% MOTP\(\uparrow\) & TP\(\uparrow\) & FN\(\downarrow\) & IDs\(\downarrow\) & \% MT\(\uparrow\) & \% ML\(\downarrow\) & FM\(\downarrow\) \\ \hline
5/10 & 4/10 & 1/10 & 44.93 & 87.84 & 56,677 & 6223 & 54,772 & 848 & 12.3 & 33.0 & 946 \\
5/10 & 3/10 & 2/10 & 44.99 & 87.84 & 56,713 & 6196 & 54,757 & 827 & 12.3 & 33.3 & 932 \\
4/10 & 3/10 & 3/10 & 44.85 & 87.76 & 56,688 & 6323 & 54,776 & 833 & **13.0** & **32.8** & 953 \\
3/10 & 4/10 & 3/10 & 44.64 & 87.71 & 56,613 & 6479 & 54,832 & 852 & 12.8 & 33.0 & 948 \\
3/10 & 3/10 & 4/10 & 44.58 & 87.70 & 56,558 & 6492 & 54,858 & 881 & 12.8 & 33.0 & 967 \\
4/10 & 5/10 & 1/10 & 44.75 & 87.75 & 56,627 & 6379 & 54,793 & 877 & 12.6 & 33.3 & 961 \\
6/10 & 3/10 & 1/10 & 45.25 & 87.90 & **56,803** & 5984 & **54,695** & 799 & 12.1 & 33.0 & 912 \\
6/10 & 2/10 & 2/10 & 45.25 & 87.92 & 56,801 & 5990 & 54,705 & 791 & 12.1 & 33.0 & 907 \\
**7/10** & **2/10** & **1/10** & **45.53** & **88.09** & 56,552 & **5426** & 54,996 & **749** & 12.6 & 33.5 & **853** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Evaluation of the \(W_{M}\) data association cost matrix using different weight combinations on the MOT17 dataset. results, the best MOTA result was achieved by the proposed \(W_{M}\) cost matrix with 45.67%. The \(W_{M}\) cost matrix reached the best results for the TP and MT evaluation metrics. The default IoU cost matrix achieved the best MOTP, FP, IDs, and FM results, which are very similar to the results attained by the proposed \(W_{M}\) cost matrix. Overall, promising results were achieved by the proposed cost matrices, being able to outperform the default IoU cost matrix. Moreover, the Deep-SORT with \(W_{M}\) cost matrix was able to obtain the highest MOTA and MT. Attained results show similar overall performances between SORT and Deep-SORT. However, as expected, SORT is much faster than Deep-SORT.