## 1 Introduction  
Object tracking is essential in computer vision, emerging immediately after object detection [1]. To accomplish object tracking, locating the target within a frame and assigning a unique ID to each object is imperative. Subsequently, every identical object across consecutive frames will form a trajectory. Here, objects can vary from pedestrians, vehicles, and athletes in motion to birds in the sky. When the task involves tracking multiple objects across frames, it is referred to as Multi-Object Tracking (MOT). In Multi-Object Tracking, the algorithm must detect all targets in every video frame, match newly detected targets with those already assigned trajectories, and classify matched targets according to existing trajectories. For unmatched targets, they are considered new and are assigned a new IDalong with a new trajectory. Targets that leave the video area are no longer tracked, and their trajectories are removed from the collection. With the advancements in science and technology, object-tracking technology is becoming increasingly vital in everyday life [2]. Panoramic Multi-Object Tracking represents a particularly challenging research domain. It encompasses not only the traditional issues faced by Multi-Object Tracking, such as object occlusion, identity switches, and the need for real-time processing, but also introduces unique challenges due to the panoramic viewpoint. Panoramic object tracking techniques, which provide a 360-degree view, offer a more comprehensive perspective for understanding dynamic information in complex scenes, significantly expanding their potential and impact in various practical applications. Current methods in Multi-Object Tracking (MOT) can be categorized into detection-based tracking and joint detection and tracking methods. Detection-based tracking, which involves detecting an object and then tracking its movements on each video frame, separates the detection and tracking stages. This approach often achieves better detection metrics due to its sequential process. On the other hand, the joint detection and tracking approach integrates the tracking phase into the object detection process, incorporating elements such as appearance feature extraction and motion offset prediction. This integration allows for the partial parallel processing of detection and tracking, thereby reducing the algorithm's time complexity. To ensure tracking accuracy, this study focuses on the detection-based tracking method. In 2017, [PERSON] et al. introduced the DeepSORT [3] tracking algorithm, which builds on the SORT algorithm by incorporating deep learning feature representations to enhance tracking performance. It primarily addresses identity switches through appearance information and introduces a more complex motion model to handle occlusions. In 2020, [PERSON] et al. proposed the CenterTrack [4] algorithm, which predicts the movement of targets directly between video frames without the need for explicit motion models or object detection. By tracking changes in the center points and movements, this method effectively handles target tracking in static and dynamic scenes. In 2021, [PERSON] et al. introduced a plug-and-play tracking module, ByteTrack [5], which improves tracking performance by adding a simple, yet efficient, byte-tracking strategy on top of the YOLOX detector. Its core idea is to utilize low-score but high-quality detection boxes to assist tracking, significantly enhancing tracking accuracy and robustness in complex scenes. In the same year, [PERSON] et al. presented an end-to-end object detection and tracking method using transformers. MOTR [6] leverages the transformer architecture to address the Multi-Object Tracking problem, enhancing the tracking performance by establishing long-term temporal dependencies. Its use of the transformer's self-attention mechanism effectively encodes the relationships between objects and their historical information. In 2023, [PERSON] et al. improved the SORT algorithm and proposed observation-centric SORT [7].