# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 3 Method
### Occlusions Variation  
To make a judgement on whether the SSD model, the Faster RCNN model and the YOLOv5 model are adaptive to different degrees of occlusion, we identified an image as  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Name** & **Brightness** & **Multiple Detection Error** \\ \hline
**Faster RCNN** & \(-5\)–\(5\) & Error \\ \hline
**SSD** & \(-5\)–\(5\) & No error \\ \hline
**YOLOv5** & \(-4\)–\(5\) & No error \\ \hline \hline \end{tabular}
\end{table}
Table 1: Testing results for deep learning models’ brightness.  
Figure 3: Examples of Screenshots of erased data set in different angles and different environments (**a**–**x**).  
the testing sample. The size of the image is 4160 \(\times\) 3120. The upper-left and lower-left corners of the target are (1,991,811) and (2752 \(\times\) 1730) respectively. We then cut the image step by step to ensure that varying occlusion of the target can be measured. The precision is controlled to the range of 1%. The experiments demonstrate that the Faster RCNN and the SSD models can handle occlusion of up to 95% under a relatively simple background. The YOLOv5 model works under up to 96% occlusion. The experiments demonstrate that the Faster RCNN can handle an occlusion of 91%. The SSD models can handle occlusion of about 80%. The YOLOv5 model works under 82.5% occlusion.  
It is worth noting that the Faster RCNN model has a problematic result in the experiments. It usually marks the local features with multiple anchors on the same object during our experiments because the model is based on the candidate box extracting. This can be seen in Figure 4: Duplicate detection in Faster R-CCN. Local features make target detection a problem for multi-scale and small targets. Multi-scale problems means that the problem studied involves multiple orders of magnitude. The feature maps extracted by Faster RCNN are all monolayers, so it is not suitable with this problem. In addition, in order to avoid re-detection, the method used by the Faster RCNN model is unfriendly to the occluded target. If the threshold is set too large, the model will result in missed detection. If the threshold is set too small, the model will re-detect. When the target only shows local features, the model will not miss detection because only a small part of the field of vision is the target. However, within this small local feature, the model will continue to subdivide, causing re-detection. For example, our data set detects one target, and the color feature is relatively simple. After the lower left corner is identified as a target, similar features inside the target will also be detected to cause re-detection for the target as shown in Figure 4.  
All three models can generally adapt well to the practical occlusion. The detection methods of these deep learning models are based on the local features of the target. Therefore, it also leads to some limitations. For example, if the training data set is not large enough, the model cannot distinguish objects of similar features from the target. However, if the training data set can provide enough possible interference and possible similar objects, the deep learning model can be more adaptive to occlusion. More details on the experiments and discussions are provided in Section 4.