## 2 Materials and Methods
### Tracking Method
#### 2.5.3 Tracking Model
1 Initialization: \(T{\leftarrow}\theta\)  
2 For frame \(\xi_{\text{k}}\) in V:  
3 \(\text{D}_{\text{k}}{\leftarrow}\text{Deft}(\xi_{\text{k}})\), \(\text{D}_{\text{high}}{\leftarrow}\theta\), \(\text{D}_{\text{low}}{\leftarrow}\theta\), \(\text{F}_{\text{high}}{\leftarrow}\theta\) /*Classify detection results*/  
4 For d in \(\text{D}_{\text{k}}\):  
5 If d.score \(>\) \(\tau\), then  
6 \(\text{D}_{\text{high}}{\leftarrow}\text{D}_{\text{high}}{\cup}[\text{d}]\) /*Store high-score detection boxes*/  
7 \(\text{F}_{\text{high}}{\leftarrow}\text{F}_{\text{high}}{\cup}\text{Enc}(\xi_ {\text{k}},\text{d.box})\) /*Extract target appearance features*/  
8 Otherwise  
9 \(\text{D}_{\text{low}}{\leftarrow}\text{D}_{\text{low}}{\cup}[\text{d}]\) /*Store low-score detection boxes*/  
/*predict new locations of tracks*/  
10 \(A_{\text{k}}^{k}\) - \(\text{f}_{\text{k}}\)- \(\text{f}_{\text{k}}\)- \(\text{f}_{\text{k}}\)/ *Calculate the affine matrix from frame k-1 to k*/  
11 \(\text{for}\ \text{in}\ \text{T}\): /*Predict the position of the track in the next frame*/  
12 \(\text{t}{\leftarrow}\text{KalmanFilter}(\text{t})\), \(\text{t}{\leftarrow}\text{MotionCompensation}(\text{t},\ A_{\text{k-1}}^{k})\)  
/*First association*/  
13 \(\text{C}_{\text{iou}}\) - \(\text{IOUDist}(T.\text{boxes},\text{D}_{\text{high}})\)  
14 \(\text{C}_{\text{emb}}\) - \(\text{FusionDist}(T.\text{features},\text{F}_{\text{high}},\text{C}_{\text{iou}})\)  
15 \(\text{C}_{\text{high}}\) - \(\text{min}(\text{C}_{\text{iou}},\text{C}_{\text{emb}})\)  
16 \(\text{Associate using the Hungarian algorithm}\)  
17 \(\text{D}_{\text{remin}}\) - remaining object boxes from \(\text{D}_{\text{high}}\)  
18 \(T_{\text{remin}}\) - remaining tracks from \(T\)  
/*Second association*/  
19 \(\text{C}_{\text{low}}\) - \(\text{IOUDist}(T_{\text{remin}},\text{boxes},\text{D}_{\text{low}})\)  
20 \(\text{Associate using the Hungarian algorithm}\)  
21 \(T_{\text{re-remain}}\) - remaining tracks from \(T_{\text{remain}}\)  
/*Third association*/  
22 \(\text{If}\ \text{D}_{\text{remin}}\text{\in}\text{D}_{\text{cor}}\), then  
23 \(\text{F}_{\text{cor}}\) - \(\text{F}_{\text{cor}}{\cup}\text{Enc}(\xi_{\text{k}},\text{D}_{\text{cor}}. \text{box})\)  
24 \(\text{C}_{\text{emb}}\) - \(\text{FusionDist}(T_{\text{re-remain}}.\text{features},\text{F}_{\text{cor}})\)  
25 \(\text{Associate using the Hungarian algorithm}\)  
26 \(\text{If}\ \text{D}{\in}\text{D}_{\text{cor}}\), then /*Update Kalman parameters*/  
27 \(\text{Initialize Kalman filter}\)  
28 \(\text{Otherwise}\)  
29 \(\text{Update matched tracklets Kalman filter}\). 30 \(T{\leftarrow}T\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{\text{ \text{\tion frames are usually caused by occlusion or motion blur, the appearance characteristics are unreliable. Therefore, the second matching selection was based on IOUs only, and the matching method was still the Hungarian algorithm. We put the unmatched tracks into \(\tau_{\text{re-remin}}\) for the next association. The third correlation compared the remaining trajectory (\(\tau_{\text{re-remin}}\)) after the second correlation and the detection frame (D\({}_{\text{cor}}\)) in the video's corner. Since there were cases in the panoramic scene where the target disappeared from the bottom left corner and appeared from the bottom right corner or disappeared from the bottom right corner and appeared from the bottom left corner, in which case it was difficult for the motion model to predict the correct position, this algorithm decided to use only [PERSON]'s method for association for this matching. At the end of the third association, the trajectories not successfully matched with the detection box were removed from the trajectory list. Finally, we determined the detection frames not yet associated with a trajectory after three matches. Detection frames with scores higher than a set threshold (\(\eta\)) generated new trajectories.