# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 1 Introduction  
The primary area of intelligent and networked development in the vehicle and transportation industries is autonomous driving. AVs have the potential to fundamentally alter transportation systems by averting deadly crashes, providing critical mobility to the elderly and disabled, increasing road capacity, saving fuel, and lowering emissions [1, 2]. The vehicle perception system's accurate perception of the environment is essential for safe autonomous driving. The perception of autonomous driving settings depends heavily on object tracking, a high-level vision job in the discipline of computer vision. As a result, the development of an object tracking algorithm ensures the development of an automatic driving system that is both safer and more effective. This section of the review focuses on multi-object tracking in autonomous driving systems. Multi-object tracking is crucial to ensuring the effectiveness and safety of autonomous driving because it is the fundamental component of the technology. Rarely do objects in traffic situations appear alone. Autonomous driving frequently involves recognizing and tracking many things at once, some of which may be moving in relation to the vehicle or to one another. The majority of techniques in the related literature therefore deal with many objects and attempt to address the multi-object tracking issue. In essence,the MOT algorithm can be summarized as: given the data collected by one or more sensors, how to identify multiple objects in each frame of data and assign an identity to each object, and match those IDs in subsequent data frames [3]. An example of the output of the MOT algorithm is shown in Figure 1 below. The research on multi-object tracking for autonomous driving has advanced significantly in recent years, but it is still challenging to use the current multi-object tracking techniques for autonomous driving to their full potential because of issues including the varied shapes of cars and pedestrians in traffic scenes, motion blur, and background interference. There are still several difficulties with the existing visual multi-object tracking technology. Visual multi-object tracking must first address more challenging problems such as: an unpredictable number of objects, frequent object occlusion, challenging object differentiation, etc. In particular, the frequent entry and exit of objects from the field of view is a typical and expected behavior in autonomous driving applications, which results in the uncertainty of the number of objects faced by multi-object tracking and necessitates real-time detection of multi-object tracking algorithms. The method must extract robust object features and keep the object-specific ID after occlusion in complicated situations, since the occlusion of an object by other objects or backgrounds will lead to object ID switches (IDs). The high degree of similarity in object appearance also adds to the difficulty of maintaining the right object ID over the long term. The algorithm must be able to extract the characteristics of comparable items that make them separable. Finally, the challenges that multi-object tracking in autonomous vehicles face can be broken down into two categories: the tracking object factor and the backdrop factor. Shape change, scale change, motion blur, etc. are some of the issues brought on by the object's factors. The impact of backdrop elements is also substantial, particularly the blurring of background interference, occlusion and disappearance of objects, changes in weather, comparable background interference, etc. [4]. This paper introduces algorithms that perform multi-object tracking using the capabilities of deep learning models, concentrating on several approaches for various MOT algorithm frameworks. We concentrate on 2D data extracted from videos shot with a single camera in this evaluation, even though the MOT job may be used with both 2D and 3D data, as well as in single-camera and multi-camera situations.