## 3 Results and Discussion
### Experiments Using the Multi-Object Tracking Algorithm  
In this study, we utilized an improved version of YOLOX_s as the detector for the proposed model. YOLOX stands out among the YOLO series algorithms due to its novelty, rich model weights, excellent real-time detection speed, precise detection performance, and unique approach to decoupled head processing. Before deploying it, we conducted mixed training on the MOT17, MOT20, and custom panoramic datasets to obtain a pre-trained model. For the re-identification (ReID) model used for pedestrian re-identification, the SBS-S50 model from FastReID was selected and trained on the MOT17 dataset to acquire a pre-trained model. The tracking association model employed a newly proposed method in this study. All parameter settings were chosen through experimentation. This approach leverages the strengths of YOLOX_s in handling diverse and challenging scenarios by enriching its training with a mix of standard and custom datasets, enhancing its adaptability and performance in real-world applications. Including a robust ReID model further supports the tracking process by improving the accuracy of identity recognition across different frames, which is particularly crucial in dense scenes or scenarios with frequent occlusions. Figure 6: Detector training results. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{1}{c}{**Metrics**} & **AP50** & **AP50\_95** \\ \hline YOLOX\_s & 0.88 & 0.64 \\ YOLOX\_s (upgrade) & 0.91 & 0.66 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Model metrics before and after YOLOX_s optimization. Following inputting the collected video material, the outcomes were subjected to target tracking by the Multi-Target Tracking algorithm after target detection. This study selected pedestrian targets captured indoors and outdoors on campus as the focus of the investigation. The algorithm's performance was assessed using three evaluation metrics: MOTA (Multiple Object Tracking Accuracy), IDF1 (IDF1 Score), and IDSWs (ID Switches). MOTA measures the overall tracking accuracy, considering false positives, false negatives, and ID switches. IDF1 evaluates the identification accuracy by considering the precision and recall of correctly identified detections over the total number of ground-truths and computed detections. IDSWs count the number of times the algorithm incorrectly changes the ID of a tracked object. The experimental outcomes are presented in Table 4. To demonstrate the advancement of this study, the ByteTrack algorithm will be used as a control group for experimental comparison and result visualization. The experimental results are shown in Figures 7-10. Figures 7 and 8 showcase the tracking results of the comparative and proposed algorithms in an outdoor setting, respectively. Similarly, Figures 9 and 10 illustrate the tracking outcomes of the comparative algorithm and proposed algorithm in indoor environments. Figures 7 and 9 show that targets passing near scene boundaries often lead to tracking failures due to large positional spans. For instance, in Figure 7, targets indicated by red arrows as numbers 12 and 15 experience tracking failure after 30 frames, with their IDs changing to 20 and 21. In contrast, in Figures 8 and 10, targets indicated by green arrows are correctly tracked throughout. These results demonstrate that the improved algorithm effectively addresses the issue of tracking failures caused by targets crossing boundaries in panoramic scenes, achieving continuous tracking results. This clear distinction in performance underscores the effectiveness of proposed algorithmic enhancements, particularly in handling the complexities of tracking across scene boundaries. The proposed algorithm's ability to maintain consistent tracking of targets through these transitional spaces significantly improves its utility for real-world applications, where subjects frequently move in and out of the camera's field of view or across distinct spatial zones.