# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
###### Abstract
They analyzed the way in which partial occlusion affects the recognition of facial expressions by human observers. [PERSON] et al. [15] developed a novel vehicle detection based on vehicle part-based proposals generation and Part Affinity Fields (PAFs)-based combination algorithm to solve the visual detection problem of occlusion in complex backgrounds. The designed algorithm improves the performance of accuracy, especially when vehicles are heavily occluded. [PERSON] and [PERSON] [16] provided a review of the strengths and weaknesses of Deep Nets for vision from 2018 to 2020. It was proven that even though real-world image sets are infinitely large and any data set (no matter how big) is hardly representative of real-world complexity, using a DL model for visual problems can solve specific visual tasks and has the potential for practical applications. In view of the previous research, the following research questions are addressed in this paper: (1) whether the deep learning model tracking methods, Faster RCNN, SSD and YOLOv5, can adapt to various occlusion conditions and (2) to what extent occlusion could be handled in a specific environment. To address these issues, a training data set of the robot target under designated mobility and occlusion conditions needs to be constructed in our research. Different testing data sets are also created. We then focus on three recent versions of DL models, You Only Look Once (YOLO) model--YOLOv5, Faster Region Proposal Convolutional Neural Network (R-CNN) and Single Shot MultiBox Detector (SSD), and compare the performance of the three DL models in the robot target tracking. A Random Erasing method is proposed to generate some testing data to mimic random interference items that block the view of the target. Different dynamic occlusion variation situations have also been tested where part of the targets is out of range. The missing portion is about 10% to 30% of the target. The environment contains indoor and outdoor environments with different backgrounds. The contributions of the article are summed up as follows: (1) We build three evaluation data sets for training and algorithm verification under single target tracking scenarios. (2) We construct three recent object detection models, which are Faster RCNN, SSD and YOLOv5. Comprehensive experiments are conducted to evaluate the detection performance with our data sets. (3) We design a Random Erasing method to create a new testing data set with various partial occlusions to verify the system performance. (4) We use image views from different angles, distances and surrounding environments to test the model performance. (5) We numerically analyze the adaptivity of the models against different degrees of occlusions through experiments. The paper is organized as follows. In Section 2, the background of deep learning technologies used in the research is introduced. Comprehensive literature review has been carried out to demonstrate why the three DL models were chosen. In Section 3, the Random Erasing method is presented. Three occlusion scenarios are also classified. In Section 4, experimental settings, experimental results and discussions are presented. The conclusion and future work are included in Section 5.