## 2 Materials and Methods
### Dual Data Association  
The multi-pedestrian tracking algorithm is aimed at continuously localizing multiple pedestrians in a video sequence, maintaining the identity of each pedestrian consistently across video frames, and generating pedestrian motion trajectories. The most commonly used tracking algorithm uses the Kalman filter to process video data frame by frame, and the Hungarian matching algorithm matches the pedestrian detection boxes and appearance feature vectors output from the detection network in the current frame with the pedestrian position predicted by the Kalman filter in the current frame across frames. The cascade matching method is used for the problem of associating detected pedestrians in the current frame with already existing tracking trajectories, and pedestrians that appear more frequently are prioritized for matching to solve the probability dispersion problem of continuous prediction. \[X_{k}=AX_{k-1}+BU_{k-1}+W_{k-1} \tag{5}\]  
\[Z_{k}=HX_{k}+V_{k} \tag{6}\]  
\[\widehat{X}_{k}=A\widehat{X}_{k-1}+BU_{k-1} \tag{7}\]  
Figure 4: Improved channel attention mechanism module; **(a)** flowchart of SENet; **(b)** flowchart of AM-Block module. \[P_{\overline{k}}=AP_{k-1}A^{T}+Q \tag{8}\]  
\[K_{k}=\frac{P_{\overline{k}}H^{T}}{H\overline{k}H^{T}+\overline{R}} \tag{9}\]  
\[\hat{X}_{k}=\hat{X}_{\overline{k}}+K_{k}\big{(}Z_{k}-H\hat{X}_{\overline{k}} \big{)} \tag{10}\]  
\[P_{k}=(I-K_{k}H)P_{\overline{k}} \tag{11}\]  
The equations of the Kalman filter are given as Equations (5)-(10), where \(\hat{X}_{\overline{k}}\) represents the a priori state estimate, which is predicted from the optimal estimate at the previous moment to the current moment; the \(Z_{k}\) in Equation (6) represents the actual detected pedestrian motion state by CenterNet at the moment t, \(Z_{k}\) is detected by the CenterNet detection network and \(X_{k}\) is calculated by the Kalman filter. Equation (7) is the covariance matrix of Equation (6), and Equation (8) is the covariance matrix of Equation (5). By using the covariance matrix of the theoretical value in Equation (5) and the actual measured values in Equation (6), we can know their stability, respectively. In the pedestrian tracking process, in order to determine whether the theoretical value in Equation (5) or the detected value in Equation (6) is accurate, the equation shown in Equation (10) is established. \(\hat{X}_{k}\) represents the optimal estimate, \(Z_{k}-H\hat{X}_{\overline{k}}\) represents the difference between the pedestrian motion state predicted by the Kalman filter and the motion state detected by the CenterNet detector, so \(K_{k}\) represents the weight, whose value is close to the true value, which value of the weight is greater. It can be seen in Equation (9) that \(K_{k}\) is larger when the covariance \(P_{\overline{k}}\) of the theoretical value is larger, and at the same time, we can see by Equation (10) that the proportion of the theoretical value is smaller when \(K_{k}\) is larger; after obtaining the measured and theoretical values and their differences, we update the parameters of the Kalman filter using Equation (11). However, there is an occlusion problem in real traffic scenarios, and mutual occlusion between pedestrians leads to a lower detection frame score for occluded pedestrians. In Figure 5, the pedestrian with ID number 4 is gradually blocked from frame 77 to frame 95, and the detection frame score gradually decreases from 0.8 to 0.1. Therefore, although the pedestrian is detected in frames 91 and 95, this pedestrian is discarded because the detection frame score of 0.1 is less than the confidence threshold, resulting in a trajectory interruption. Thus, it is not reasonable to discard the low-scoring pedestrian detection frame based only on a frame confidence threshold.