## 2 Materials and Methods
### Dual Data Association
In this paper, we set the confidence threshold of the detection boxes to 0.01 to keep the low-scoring detection boxes, and for the high-scoring detection boxes, we followed the current common data association method, Then, we used the Hungarian algorithm to match it optimally. The low-scoring detection boxes were associated with the trajectories that were not successfully matched with the high-scoring detection boxes in the previous frame for the second time; then, the Hungarian algorithm was used again to match the low-scoring pedestrian detection boxes with the existing trajectories. The purpose is to pull out partially occluded pedestrians from low-scoring detection boxes, optimize the trajectory interruption problem caused by occlusion during the tracking process, and maintain the continuity of the trajectories. Figure 5: Dropping a low-scoring detection frame causes a trajectory break; (**a**) 77 th frame; (**b**) 91 st frame; (**c**) 95 th frame. For the convenience of calculation, assume that the pedestrians in the video are moving in a uniform motion, and the flow chart of the improved tracking algorithm is shown in Figure 6, it has the following steps:  
Step 1: Output the detected boxes and apparent feature vectors of all pedestrians detected in the current frame through the anchor-free detection network, and set the confidence threshold to 0.01 to retain both high and low-scoring detected boxes;  
Step 2: Use the Kalman filter to predict the pedestrian motion state of the current frame and set a tracker for each existing track. If the Kalman filter predicts the pedestrian motion state of the current frame with a matching high-scoring pedestrian detection box, the counter of the tracker is set to 0; conversely, if the tracker does not find matching detection boxes for a period of time (max_age > 30), the tracker is deleted and the line of people leaves the video screen by default. Step 3: At the same time, when a new pedestrian appears in a frame, a new tracker is created for the pedestrian. If the motion state predicted by the Kalman filter can find a matching detection box for three consecutive frames, the new pedestrian is confirmed and the tracker state is set to \"confirmed\"; otherwise, it is \"unconfirmed\", which generally prioritizes matching the confirmed tracker, where the matching algorithm uses the Hungarian algorithm and considers the motion information and appearance feature association of the pedestrian at the same time. Motion information correlation: Because of the continuity of pedestrian motion in the video, the degree of correlation between the high-scoring pedestrian detection box and the position predicted by the tracker is calculated using the Mahalanobis distance, as shown in Equation (12):  
\[l^{(1)}(i,j)=(l_{j}-p_{i})^{T}X_{i}^{-1}(l_{j}-p_{i}) \tag{12}\]  
where \(l^{(1)}(i,j)\) represents the match between the j-th pedestrian detection box in the current frame and the \(i\)-th trajectory predicted by the Kalman filter, \(X_{i}^{-1}\) represents the covariance matrix between the position of the pedestrian detection box and the average tracking position, \(l_{j}\) represents the position of the \(j\)-th pedestrian detection box in the current frame, and \(p_{i}\) represents the position of the trajectory predicted by the \(i\)-th tracker in the current frame. Appearance information association: In order to enhance the robustness of the model when pedestrians are obscured and then reappear, pedestrian apparent feature vectors  
\[Z_{k}=HX_{k}+V_{k} \tag{6}\]0 Flowchart of dual data association module. are introduced for data association. First, for each high-scoring pedestrian detection box output by the detection network, a 128-dimensional feature vector is extracted by the ReID module, and a list is created for each tracked pedestrian, storing the apparent feature vectors of the last 100 frames that were successfully matched.