# Convolutional Neural Networks Refitting by Bootstrapping for Tracking People in a Mobile Robot
## 4 Discussion  
The accuracy score of the CNN trained by applying the bootstrapping technique increased by 8.32% compared with the CNN trained with data labelled by KIO. In addition, the loss value has also increased by 7.93% compared to the training done with bootstrapping and the KIO.  
The confusion matrices presented in Section 3 provide an overall view of the differences between the two CNNs. As mentioned in Section 2.9, the \(T_{P}\) values match the number of people correctly detected by both CNNs, specifically, 21 and 21, respectively. The \(F_{P}\) values match the number of people wrongly detected, there are not any people at the scene but the model detects someone, by both CNNs, specifically, 16 and 3, respectively. The \(F_{N}\) values match the number of people not detected by both CNNs, specifically, 21 and 21, respectively. This field has not improved because people are far away from the robot or the LIDAR data have partial occlusions from the scene. On the other hand, the \(T_{N}\) value matches the number of cases where PeTra does not detect any people and actually, there are no people in the scene. Such cases correspond to the scenes recorded in Rosbag files 18-25. According to Figure 4, there are 4 cases where the CNN fitted with KIO does not return people's location estimates on scenes where there are no people. Specifically, in the scenes recorded in Rosbag files 18, 21, 22, and 23. In the other four cases, the CNN fitted with KIO return people location estimates where there are none. For the CNN refitted by bootstrapping, Figure 5 shows that in all cases (8), the system does not return people location estimates for scenes where there are no people.  
According to Table 3 both CNNs have the same Precision value (\(Pre=0.5\)). However, the rest of the metrics are higher with the CNN refitted by bootstrapping. The sensitivity value--an important measure for imbalanced data--is considerably lower for the CNN fitted with labelled data by using the KIO device (\(Sen=0.5676\)) compared to the CNN refitted by bootstrapping (\(Sen=0.8750\)), this represents an increase of 54.16%. The Specificity value for the CNN refitted by bootstrapping (\(Spe=0.2759\)) is 72.43% higher compared to the CNN fitted with labelled data by using the KIO device (\(Spe=0.1600\)). The accuracy value is also lower for the CNN fitted with labelled data by using the KIO device (\(Acc=0.4032\)) compared to the CNN refitted by bootstrapping (\(Acc=0.5472\)), thus representing an increment of 35.71%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**Prediction**} \\ \cline{3-4}  & & **No People** & **People** \\ \hline \multirow{2}{*}{**Actual**} & **No people** & 4 & 16 \\  & **People** & 21 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix of the CNN refitted by bootstrapping (CNN\({}_{bootstrapping}\)).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & \(Sen\) & \(Spe\) & \(Pre\) & \(Acc\) & \(MCC\) \\ \hline CNN\({}_{KIO}\) & 0.5676 & 0.1600 & 0.5000 & 0.4032 & \(-0.2859\) \\ CNN\({}_{bootstrapping}\) & 0.8750 & 0.2759 & 0.5000 & 0.5472 & 0.1852 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sensitivity (\(Sen\)), Specificity (\(Spe\)), Precision (\(Pre\)), Accuracy (\(Acc\)), and MCC for both the CNN fitted with labelled data by KIO (CNN\({}_{KIO}\)) and the CNN refitted by bootstrapping (\(\text{CNN}_{bootstrapping}\)).  
Finally, the \(MCC\) score is the most important KPI to consider since it engages the rate of True Positives correctly detected with a low rate of False Positives. For the CNN fitted with data labeled by the KIO device the value is lower (\(MCC=-0.2859\)) than the CNN refitted by bootstrapping (\(MCC=0.1852\)). The MCC ranges in the interval \([-1,+1]\), with extreme \(-1\) and \(+1\) values reached in case of fatal and perfect classification, respectively. This value is 65.97% higher for the CNN refitted by bootstrapping.