## 2 Literature Review  
### Multi-Object Tracking  
Multi-object tracking involves identifying moving targets in each frame of a video series and assigning the same identification label to the same object across successive frames, resulting in separate motion trajectories for individual objects [5].  
[PERSON] et al. [6] proposed the DeepSORT algorithm, which utilized a proprietary residual network trained on pedestrian re-identification datasets to extract deep appearance features mapped onto a hypersphere. These features were then used to determine the minimal cosine distance between identified and tracked targets. During the association phase, the Mahalanobis distance and cosine distance, linearly weighted according to the tracking prediction box generated from Kalman filtering and the detection box in the current frame, were used to compute a cost matrix. Cascade matching was then applied to pair the anticipated boxes with the detected boxes. This compact tracking strategy incorporated motion and appearance information, achieving high multi-object tracking accuracy (MOTA) in real time, while efficiently minimizing occlusion concerns. [PERSON] et al. [7] presented ByteTrack, a simple, efficient, and general method for data association. Instead of focusing only on high-scoring detection boxes, it associates each detection box with a track by leveraging its resemblance to pre-existing trajectories. This approach utilizes the similarities between low-scoring detection boxes and existing tracks to distinguish genuine targets from background detections. Both high- and low-scoring detection boxes were fully utilized in regard to this method, which can be directly integrated into standard tracking techniques, such as DeepSORT, resulting in varying degrees of improvement. With the growing popularity of Transformers, new techniques like TrackFormer [8] and TransTrack [9] have emerged, which combine Transformers with multi-object tracking. These techniques enhance tracking performance by drawing inspiration from the principles of Transformer encoders and decoders. However, the computational burden they introduce is significant and cannot be overlooked.  
Multi-target tracking algorithms currently have a wide range of applications. [PERSON] et al. [10] combined DeepSORT with YOLOv5 in order to follow Bactrocera minax and recognize its grooming behavior. Although this method provided a viable approach for multi-object behavior identification, the tracker still requires significant improvements. [PERSON] et al. [11] used multi-target tracking for pedestrian detection to gather data for autonomous vehicle driving. They enhanced the YOLOv7 backbone network by incorporating spatial depth convolutional layers and applied the concept of convolutional structural reparameterization to create a comprehensive feature extraction block. This optimized block was integrated into the DeepSORT tracker, resulting in an improved system for pedestrian detection. [PERSON] et al. [12] combined YOLOv8 with StrongSORT, systematically tuning the parameters across various dimensions to achieve optimal adaptability. From a vehicular perspective, these two techniques show promising results in addressing pedestrian tracking issues. [PERSON] et al. [13] introduced a new Intersection over Union (IoU) calculation method and applied it to SORT, DeepSORT, and other methods, achieving excellent results in regard to the multi-object tracking of ships in waterways. However, due to the limited experimental circumstances employed to evaluate this strategy, its generalizability cannot be guaranteed.