# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 2 Literature Review
### Ssd  
The SSD model detects objects from the images using a single deep neural network [28]. SSD has been emphasized by many researchers [22; 23; 24; 29]. The development procedure will be explained briefly. [PERSON] et al. proposed SSD300 and SSD512 and demonstrated their superiority in image detection [29]. [PERSON] et al. [22] applied SSD to implement the automatic feature learning process on the aerial image set. They pointed out that the model trained by SSD can extract high-level features and improve the detection speed. [PERSON] et al. [23] implemented SSD as the CNN detector with the Kalman filter. The Lightweight Feature Fusion Single Shot Multibox Detector (L-SSD) algorithm was proposed to improve the detection performance in a manual garbage sorting environment [24]. The basic size and shape of the priority anchor in the SSD model cannot be obtained directly through learning, but need to be set manually which may lead to human error. SSD has the advantage from the viewpoint of detection speed because the object classification and prediction anchor regression are implemented simultaneously. SSDutilizes VGG16 to extract features and samples the feature map at different locations with different scales densely and evenly. The weight parameters of VGG16 are trained in advance, so they can be loaded to the network directly. The input image is reshaped to a size of 300 \(\times\) 300 pixels before being imported into the network. Six feature map layers are extracted and default anchors with different scales are constructed on every point of the feature maps. The feature maps of low layers have smaller receptive fields which can detect small objects. The feature maps of high layers are vice versa. The goal of the procedure is to detect objects with multiple scales. The principles of choosing scales and aspects for the default anchor are critical in implementation. For the detection of the mobile robot, the size of the anchor is set to be \(S_{k}=[30,\,60,\,111,\,162,\,213,\,264,\,315]\) and \(k\in[1,\,7]\). The unit of the anchor size is one pixel. K is the number of convolution layers. The feature maps of Conv4_3, Conv7, Conv8_2, Conv9_2, Conv10_2 and Conv11_2 layers are extracted and six default anchors with different scales are constructed on every point of the feature maps. The feature maps of lower layers have smaller receptive fields which can detect small objects, while feature maps of higher layers have larger receptive fields that can detect large objects. The goal of the procedure is to detect objects with multiple scales. The principles of choosing scales and aspects for default anchors are important. Six feature maps have a different number of anchors on every point. For feature maps from Conv4_3, Conv10_2 and Conv11_2 layers, every point has four anchors. If the feature map k has four anchors, aspect ratios are denoted as \(a_{r}\in\{1,\,2,\,1/2\}\), \(r\in[1,\,3]\). We can compute the width and height of the four anchors as:  
\[W_{k}^{r}=S_{k}\sqrt{a_{r}} \tag{2}\]  
\[h_{k}^{r}=\frac{S_{k}}{\sqrt{a_{r}}} \tag{3}\]  
For \(a_{r}=1\), another square anchor is added whose size length is \(\sqrt{S_{k}S_{k+1}}\). If feature map \(k\) has six \(a_{r}\) anchors, aspect ratios are denoted as \(a_{r}\in\{1,\,2,\,1/2,\,3,\,1/3\}\), \(r\in[1,\,5]\). In training, the target object, the overall objective loss function is used [19]. The overall objective loss function is a weighted sum of the localization loss of positive samples, the confidence loss of positive samples and the confidence loss of negative samples:  
\[L_{total}=\frac{1}{N}\Big{(}L_{pos\_conf}+L_{pos\_loc}+L_{neg\_conf}\Big{)} \tag{4}\]  
\(N\) is the number of matched default anchors, \(L_{total}\) is the overall objective loss function, \(L_{pos\_conf}\) is the confidence loss of positive samples, \(L_{pos\_loc}\) is the localization loss of positive samples and \(L_{neg\_conf}\) is the confidence loss of negative samples [19]. The possibility of each anchor which does not include objects and is not part of the background is calculated.