# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 1 Introduction
Visual multi-object tracking has made significant strides in recent years due to significant improvements in the detection techniques that use deep learning. Deep learning-based multi-object tracking algorithms have also been used in the area of autonomous driving with considerable success. There are, however, not many more thorough reviews of visual multi-object tracking. Some of the currently used relevant reviews are based on intricate data association algorithms and conventional techniques, which are very unlike the widely used multi-object tracking techniques currently in use [4; 5]. A component of the more recent emphasis on detection-based tracking techniques for visual multi-object tracking is based on deep learning [6; 7; 8; 9]. As far  
Figure 1: An illustration of the output of an MOT algorithm. Each output bounding box has a number that identifies a specific object in the video. as we are aware, there is not a relevant review that compares and contrasts the joint detection and tracking algorithms and the Transformer-based multi-object tracking techniques. Some reviews and surveys have been published on the subject of MOT. Their main contributions and limitations are the following:  
* Luo et al. [5] presented the first in-depth analysis of MOT, particularly pedestrian monitoring, which was groundbreaking. They provided a comprehensive explanation of the MOT problem and outlined the primary methods applied in the crucial MOT system steps. Considering that only a small number of algorithms were using deep learning at the time, they saw it as one of the future research directions. More details were provided regarding complex data association algorithms and conventional methods;
* Ciaparrone et al. [6] offer the first in-depth analysis of how deep learning is used in multi-object tracking. They mainly concentrated on using the MOT algorithm's four phases to apply deep learning techniques. The track management module, nevertheless, which is a crucial component in TBD-based algorithms, was disregarded;
* Sun et al. [7] conducted a thorough analysis of current developments in TBD-based MOT algorithms. They thoroughly described each step of the process and performed a rigorous analysis of the TBD algorithms now in use. The JDT algorithm was regarded as the development trend in the final analysis. In this paper, based on the discussed limitations, our aim is to provide a survey with the following main contributions:  
* In this article, we explore the use of deep learning-based visual multi-object tracking algorithms in autonomous driving. We concentrate on 2D data derived from single-camera movies and discuss current work that has not been previously surveyed or reviewed. In the visual MOT algorithm, we first identified three frameworks: TBD, JDT, and Transformer-based tracking. As a quick reference for future research, we list the various deep learning models and techniques utilized in each framework and illustrate how they differ from one another. * In order to compare them numerically and uncover the key trends in the top performing methods, we gather experimental results from the most popular MOT datasets. * As a final point, we discuss the possible future directions of research. Compared to other studies, this paper offers a thorough overview of the problems, datasets, and algorithms involved in visual multi-object tracking in autonomous driving scenarios. It also offers a theoretical framework for algorithm research by experts in related topics. Additionally, the method we discuss is intended to open a door for the quick development of affordable and safe autonomous driving, because it simply uses monocular cameras as opposed to sophisticated sensor fusion.