# Mobile Robot Tracking with Deep Learning Models under the Specific Environments
###### Abstract  
Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric \(P\) is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest \(P\) score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest \(P\) scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design. deep learning (DL); computer vision; robot tracking +
Footnote †: journal: Applied Sciences  
average (ARMA) model was raised by [PERSON]'s team [5]. A motion-planning framework for urban autonomous driving at uncontrolled intersections was presented by [PERSON] and [PERSON] [6]. However, processing speed and anti-blocking properties of these deep learning (DL) methods have not been paid enough attention in the mobile robot tracking area. To solve the underlying optimization problems, neural network (NN) based models, especially convolutional neural network (CNN) models, recurrent neural network (RNN) and graph neural network (GNN) models, can be adopted [7]. Most of the research approaches with NN-based methodologies in target tracking problems have focused on solving filtering in target tracking, target drift and target loss and improving the tracking precision, especially in low-quality videos [8; 9; 10]. In a dynamic scenario, we only have initially knowledge of the robot we want to track. The data set of the target robot in the indoor and outdoor environment may be collected for training. The robot will then be deployed in an unknown environment. However, during the maneuver of the robots, fast target motion and unknown interference items present in the scene might cause severe occlusion of the robot target. This imposes the major challenge in NN-based target tracking. Various solutions have been proposed to resolve the occlusion problem. [PERSON] and [PERSON] [11] designed an end-to-end deep occlusion network (DOC) for estimating occlusion relations which enable better training and testing of deep networks for occlusion estimation. [PERSON] et al. [12] developed a computational model for figure/ground assignment in a complex natural scene. The model provided a feasible approach to bottom-up figure/ground assignment in natural images. [PERSON] et al. [13] raised an approach to recover the occlusion boundaries and depth ordering of free-standing structures in the scene. The research pointed out a research direction for single-image occlusion reasoning and the broader 3D scene understanding problem. [PERSON] et al. [14] addressed the effect of partial occlusion on facial expression recognition.