## 1 Introduction
This study rethinks and refines the classic SORT algorithm, introducing an observation-centric approach to SORT. This method enhances the robustness of Multi-Object Tracking by optimizing the matching process between the detected and tracked objects. The core idea is to emphasize observational data during the tracking phase, using improved data association strategies to reduce identity switch issues, thereby achieving a more stable tracking performance in complex dynamic environments. In the same year, [PERSON] et al. proposed the TransMOT [8] algorithm, which models the spatial and temporal interactions between objects using a graph transformer model. By organizing object trajectories in the form of sparse weighted graphs, the algorithm effectively captures interactions between a large number of objects. Additionally, in 2023, [PERSON] et al. introduced the MOTRv2 [9] algorithm, an improvement on MOTR. By introducing additional object detectors and employing a Query Anchor Box strategy, along with proposing Proposal Anchor Boxes, the algorithm optimizes the conflict between detection and association tasks. This study conducted an in-depth investigation of current Multi-Object Tracking (MOT) methodologies and analyzed their key challenges. Subsequently, this study delved into Multi-Object Tracking issues within panoramic scenes using deep learning-basedalgorithms. Given the limitations of current tracking methods in panoramic settings, this study has made several improvements. Specifically, the main contributions of this study are as follows:  
1. To address the challenges of significant target scale variation and changing lighting conditions in current datasets, this study proposes a Multi-Object Tracking algorithm incorporating an improved detector. In terms of model architecture, this study introduces a new small object detection layer, specifically designed to identify targets far from the capturing device and smaller in size, thus compensating for the deficiencies of traditional detection methods in such scenarios. Furthermore, to capture and analyze target features, this study more accurately integrates the Polarized Self-Attention (PSA) [10] mechanism during the object detection phase. This mechanism, by adjusting the model's focus on features, significantly enhances the effectiveness of feature extraction, ensuring that the algorithm maintains efficient and accurate tracking performance in scenes with dim lighting or significant changes in lighting. These optimizations and improvements enhance the adaptability and robustness of the tracking algorithm and provide robust support for high-precision target tracking under various environmental conditions, significantly improving the algorithm's reliability and effectiveness in practical applications. 2. The improvement efforts in the tracker aspect are based on the distinguished multi-object tracker, ByteTrack, which introduces an efficient data association technique known as BYTE, easily integrated into existing tracking systems for consistent performance enhancement. ByteTrack conducts object tracking based on the object detector YOLOX [11] and the appearance feature extraction module FastReID [12] SBS-S50, demonstrating strong occlusion robustness through its precise detection capabilities and optimized low-score detection box association strategy. However, ByteTrack exhibits limitations when dealing with panoramic scenes, leading to performance degradation. To address this issue, this study deeply refines the tracking strategy, imposing restrictions on specific areas within the scene and employing ReID technology for object matching. Additionally, this study introduces a camera motion estimation module to overcome the target distortion caused by camera shake. These enhancements significantly improve the algorithm's tracking performance in panoramic scenes. 3. This study has developed a comprehensive dataset for Multi-Object Tracking in panoramic scenes, encompassing indoor and outdoor campus environments.