# A Review of Deep Learning-Based Visual Multi-Object Tracking Algorithms for Autonomous Driving
## 6 MOT Algorithm Based on Transformer  
These transformer-based models with excellent representation capabilities have created a breakthrough in natural language processing (NLP). Attention methods were first \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Dataset** & **Principle** & **Advantage** & **Limitation** \\ \hline \multirow{4}{*}{[70]} & \multirow{4}{*}{Tracktor++} & MOT15 [32], & Utilize existing deep detection algorithms to integrate tracking functions into detection modules to simplify training on tracking tasks & Using the detection network to achieve the tracking function, the algorithm & \multirow{4}{*}{Too much reliance on detectors} \\  & & MOT16 [25] & modules to simplify training on tracking tasks & & accuracy and speed have \\ \cline{1-1} \cline{4-5}  & & & On the basis of CenterNet, add the image information of the previous frame to realize real-time tracking & & Run in real time without relying on apparent features and achieves high tracking accuracy & High frequency of IDs for long-term tracking \\ \cline{1-1} \cline{4-5}  & & & A network for joint learning of detection and embedding of apparent features & & Run in real time and is comparable to TBD algorithms in accuracy & The existence of anchor boxes leads to the misalignment of detection and embedded appearance features \\ \cline{1-1} \cline{4-5}  & & & Combining the complementarity of single-object tracking and data association to build end-to-end & & & \\ \cline{1-1} \cline{4-5}  & & & & & \\ \cline{1-1} \cline{4-5}  & & & & & \\ \cline{1-1} \cline{4-5}  & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Algorithm analysis and comparison based on JDT framework. presented by [79] for application to machine translation, achieving strong performance in NLP tasks. The CNN model, which combines a number of linear layers and nonlinear activations with strong feature representation capabilities, is the deep learning-based approach that computer vision experts use the most frequently [35; 41; 80]. Convolution and pooling layers are introduced by CNN to handle shift-invariant input. Convolution cannot fully utilize contextual information because it lacks a comprehensive grasp of the image itself and is unable to model the relationship between features. Convolution's weights are also fixed and unable to adjust dynamically in response to changes in the input. Recent research has used the transformer in the field of computer vision, inspired by the tremendous success of transformer design in NLP. In contrast to CNN, the transformer's self-attention mechanism is not constrained by local interactions; instead, it can aggregate data from all inputs and achieve fast parallelism. The transformer's perfect memory and global computing power also make it more appropriate for processing lengthy sequences [81]. In order to fully study the characteristics of deep neural networks and enhance the network's accuracy, the depth of the transformer can also be raised. In the past decade, attention mechanisms have played an increasingly important role in computer vision, and are used to solve a variety of vision problems, including object detection [15; 82], semantic segmentation [83; 84], 3D vision [85; 86], and are starting to achieve success in the field of multi-object tracking. The multi-object tracking problem includes the transformer architecture, which has shown tremendous success in the CV filed. We present a simplified depiction of the encoder-decoder architecture in Figure 6 to further our understanding of the integration of transformer-based multi-object tracking algorithms for trajectory queries in decoder self-attention blocks. The transformer encoder receives image information from the CNN backbone and adds spatial position encoding to the query and key of each multi-head self-attention layer.