## 3 Methods
### Association Correction Network  
In the MOT process, the target trajectory is often interrupted due to problems such as background interference and inter-target occlusion, and the target association using the Hungarian algorithm [37] may track the target, but the effect has limitations. For example, a pedestrian from the beginning of the occlusion to the end of the occlusion process, before and after the occlusion of the tracking target ID has been transformed, that is, the tracking trajectory has occurred to interrupt the situation. To address this phenomenon, this paper improves the Hungarian algorithm based on BYTE [7] and proposes a target association correction module to reduce the trajectory interruptions occurring due to occlusion, i.e., tracking the phenomenon of target ID transformations and obtaining a complete trajectory, as shown in Figure 4.  
First, the detected pedestrian detection boxes are inputted into the association network, and the thresholds, \(T_{h}\), \(T_{I}\), and \(T_{th}\), are set to denote the detection score high score threshold, low score threshold, and tracking threshold for data association matching, which are categorized into the high score detection boxes (\(I_{h}\)) and the low score detection boxes (\(I_{1}\)) through the confidence threshold. Figure 4: The overall pipeline of target association correction. Secondly, \(I_{h}\) is IoU (Intersection-Over-Union) matched with the result of Kalman filter prediction of the detection box of the previous frame to calculate the similarity between the detection box and the prediction box; the extracted Re-ID appearance features are fused with the result of IoU matching for feature fusion, and the matching optimization is carried out by using the improved Hungarian algorithm, which is named as the Weighted Hungarian Algorithm, and the weighted cost function is:  
\[C=\alpha C_{IoU}+\beta C_{\text{Re}-ID}, \tag{12}\]  
\(\alpha\), \(\beta\) are normalized weighting factors. The following three types of matching results are obtained: the successfully matched trajectory (\(T_{1}\)), the unmatched high score detection box (\(I_{\text{{1}}remain}\)), and the unmatched trajectory (\(T_{remain}\)). For \(T_{1}\), the trajectory is updated using the Kalman filter, and for \(I_{remain}\), it is initialized as a new trajectory to participate in the next association process, which completes the first association between the detection box and the prediction box. The trajectory (\(T_{remain}\)) is associated with the low-scoring detection box (\(I_{1}\)) for the second time, and in order to ensure the integrity of the trajectory and improve the robustness, the association correction module ACM is embedded. Specific description of ACM: the target ID during the tracking process is saved and the average value of the height-width of the tracking box of the tracked target is calculated as \(\overline{H}\) and \(\overline{W}\). Set the border height of target \(i\) of the current \(t\)-frame to \(B_{i}^{h}\) and width to \(B_{i}^{w}\), perform the comparison, and set the threshold value \(\in\{0.99,1.01\}\). Let the center of target \(i\) in frame \(t-1\) be \(P_{i}^{t-1}\Big{(}x_{i}^{t-1},y_{i}^{t-1}\Big{)}\); let the center of object \(i\) in frame \(t\) be \(P_{i}^{t}\big{(}x_{i}^{t},y_{i}^{t}\big{)}\). Comparisons are made using the Euclidean distance \(\theta\), \(\theta\in\{0,\,2\}\); If \(B_{i}^{h}\) and \(B_{i}^{w}\) are \(\in\{0.99,1.01\}\), and \(\sqrt{\Big{(}x_{i}^{t-1}-x_{i}^{t}\Big{)}^{2}+\Big{(}y_{i}^{t-1}-y_{i}^{t} \Big{)}^{2}}\leq\theta\), then the front and back frames are the same target. After the second association, three results are obtained: for the successfully matched trajectories (\(T_{2}\)), they are fed into the Kalman filter again to update the trajectory information. For unmatched low-score detection boxes (\(I_{remain}\)), they are treated as background deletion. For still-unmatched trajectories (\(T_{re-remain}\)), if they do not match \(I_{1}\) for 30 consecutive frame rates, they are deleted.