# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 4 Results and Discussion
Its maximum value is 1 and its minimum value is 0. In general, the larger the F1 score is, the better the model's performance. The training time is the time spent by the model to train the data set. The detection speed is how many frames the model can detect per second. As the values of the F1 score and the detection speed should be as high as possible and the value of the training time should be as low as possible, the performance of the model can be determined accordingly. The function of \(P\) can be formulated as follows:  
\[P=\frac{\text{F1 score}\times\text{DetectSpeed}}{\text{TrainingTime}} \tag{5}\] \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Training Time** & **F1 Score** & **Precision** & **Recall** & **Detection Speed** \\ \hline
**Faster RCNN** & 48.25 h & 0.87 & 93.38\% & 77.62\% & 1.6 f/s \\ \hline
**SSD** & 15.16 h & 0.89 & 97.72\% & 81.16\% & 6.6 f/s \\ \hline
**YOLOv5** & 20.25 h & 0.77 & 97.69\% & 63.66\% & 3 f/s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Testing Results for testing data set one. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Name** & **Training Time** & **F1 Score** & **Precision** & **Recall** & **Detection Speed** \\ \hline
**Faster RCNN** & 48.25 h & 0.89 & 91.55\% & 87.54\% & 1.6 f/s \\ \hline
**SSD** & 15.16 h & 0.81 & 76.96\% & 81.99\% & 6.6 f/s \\ \hline
**YOLOv5** & 20.25 h & 0.98 & 98.18\% & 98.18\% & 3 f/s \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Testing Results for testing data set two. The higher the value of \(P\), the better the model performance will be. Based on Table 2, it can be calculated that \(P_{\text{ssd}}=0.387\), \(P_{\text{faster-rcnn}}=0.029\) and \(P_{\text{Yolo}}=0.114\). Based on Table 3, the three values can be derived as \(P_{\text{ssd}}=0.353\), \(P_{\text{faster-rcnn}}=0.030\) and \(P_{\text{Yolo}}=0.145\). According to the results, it can be concluded that the SSD model has the best performance. For further analysis of whether classic deep learning model tracking methods can adapt to various occlusion conditions and to what extent occlusion could be handled in a specific environment, a video containing our target and two similar objects was created to test the performance of the Faster RCNN model, SDD model and YOLOv5 model. This is a thirty-nine second-long video with thirty frames per second. An example of the video can be seen in Figure 7: Screenshot of the testing video. At first, the target is located on the screen as shown in Figure 7a,b. Then, a similar object is placed in the screen as shown in Figure 7c,d. After that, a third similar object is placed on the screen, as shown in Figure 7e. While filming the target, the photographer constantly changes the angles and keeps a relatively close distance to the target which can be seen in Figure 7f-h. The testing results can be seen in Figure 8: Three models' testing results in the same frame. In Figure 8, the object in the lower right corner is the target. The other two objects are interferents. Figure 8 shows that in the same frame, the Faster RCNN model detected the target, but also incorrectly identified the similar objects. The SSD model successfully detected the target and ignored the similar objects. The YOLOv5 model detected the target, but incorrectly identified the similar objects and gave one of the similar objects a higher possibility than the true target. Based on the results, it can be concluded that the SSD model has the best performance in the designed experiments. The SSD model has the lowest training time, the highest F1 score, the highest precision, the highest recall and the fastest detection speed. The SSD model achieves the highest performance score in two testing data sets. Such experiments and dataset design methods can be applied to other specific circumstances. However, the some limitations still exist in the experiments. First, the environments of the created data sets are not complicated enough. Second, the created data sets are relatively small.