# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
## 2 Literature Review  
The main DL algorithms for target detection are CNN-based models and can be classified according to the number of stages: one or two stages. The two-stage approach contains the R-CNN algorithm, and the one-stage approach refers to YOLO or SSD. For the two-stage method, training data need to be generated in advance to obtain sparse data. These training candidate data are then well-tuned with classification and regression. The advantage of the two-stage method is its high accuracy. The one-stage method involves conducting dense sampling uniformly at different positions of the picture. Convolutional Neural Network (CNN) is a mathematical or computational model that mimics the structure and function of biological neural networks [17]. In object detection, the image needs to be divided into multiple regions to detect objects in this area. Therefore, CNN is introduced to select regions. Region proposals Convolutional Neural Network (RCNN) is a model that uses CNN to improve the effect of target detection. The Faster RCNN is a single, unified network for object detection composed of two modules. One is a deep, fully convolutional neural network that generates candidate boxes. The other is the Faster RCNN detector based on the proposed regions. Both the SSD model and YOLOv5 model are deep learning models for target detection based on CNN [18; 19; 20]. Different scales and aspect ratios can be used for sampling. After that, CNN can be used to extract features and directly carry out classification and regression. The whole process only needs one step. Therefore, the advantage of the one-stage method is the fast spread. However, an important disadvantage of uniform dense sampling is that it is difficult to train. This section describes the background of deep learning technologies used in this research.  
The Faster RCNN model, SSD and YOLOv5 models are selected because they are the most recognized and novel deep learning models which can be applied to visual tracking. In 2015, the Faster RCNN model won many first-place prizes in ILSVRV and COCO competitions. The algorithm was initially based on a Faster RCNN and proposes a region proposal network (RPN) candidate box generation algorithm which greatly improves the speed of target detection. The algorithm proposed by [PERSON] [18; 21] improves the comprehensive performance drastically, especially in terms of detection speed. The SSD model has the advantage in processing speed because the object classification and prediction anchor regression are done simultaneously. It utilizes CNN to extract features and densely and uniformly samples the feature map at different locations with different scales. In 2020, the Ultralytics released YOLOv5 [20; 22; 23; 24; 25]. YOLO redefines object detection as a regression problem and applies a single convolutional neural network (CNN) to the entire image. Images are divided into grids, and the class probabilities and bounding anchors of each grid are predicted. Different from the previous version--YOLOv3 and YOLOv4--YOLOv5 can predict across layers, and is still being updated by Ultralytics.