## 2 Proposed MOT Method
### Diverse and Hierarchical Matching Strategy  
The Hungarian algorithm [19] is simple and fast, and is often used in the correlation matching between trajectory and detection. However, it often matches trajectories and detections only according to the loss matrix between them, elements of which are the similarity distance, without considering the need for specific similarity in specific scenarios. For example, at a certain time, two different trajectories have the same similarity with the same detection. According to the Hungarian algorithm, since it is based on global information for correlation matching, it may assign a trajectory to a detection which should not be its match. In this case, we can use other additional information to help us infer how to match the trajectory and detection correctly. The trajectory confidence introduced above is one of the important ways. Before starting matching, we first divide the current existing trajectories according to the confidence, so that the high confidence trajectories are correlated before the confidence trajectories. We also have to divide the detections into two sets, because the above situation can be reversed, that is, two detections can compete for one trajectory. In addition, instead of dividing all existing trajectories, we must divide the trajectories in the Confirmed and Tentative states. As mentioned in Section 2.1, Vanished and Deleted states are equivalent to two situations in which the object disappears, so we don't need to consider their confidence. To sum up, we design Algorithm 1 for data association. First of all, we take the trajectory set and detections set of the current frame as input. In line 1, we divide the trajectories in different states and we calculate the confidence of trajectories in Confirmed and Tentative states according to Equations (1) and (2), and divide the trajectory sets with high and low confidence according to the confidence threshold mentioned in Section 2.2. Similarly, we divide the detection set into high and low confidence sets. In line 3, we calculate the similarity matrix between the current trajectory and the detection according to Equations (3), (4), and (5). After the preparations have been completed, in line 4 we use the Hungarian algorithm to match the high and low confidence trajectory sets and the detection set in order of their priority, to ensure that the correct matches are matched in advance, so as to reduce the number of FP. Since the drastic change of appearance caused by occlusion, illumination, or other reasons may affect the appearance characteristics of the object and make the similarity measurement inaccurate when the moving distance of the object between two continuous frames is not large, in line 5 we make IoU matching for the remaining unmatched Confirmed and Tentative trajectory sets and the detection set. In line 6, we match the trajectories in the Vanished state only according to the appearance similarity matrix. The reason for this is that the current position of trajectories in the Vanished state may be far from their positions before they disappear, and it is unreasonable to predict them in motion information. Therefore, we do not use the total similarity equation correlate here. Finally, we update the matching pairs, unmatched trajectory sets, and unmatched detection sets in lines 7-9 and return them in line 10; thus we then can update and initialize the trajectories.