# Non-Maximum Suppression Performs Later in Multi-Object Tracking
## 1 Introduction  
Multi-object tracking (MOT) is a deep learning task used to distinguish multiple targets appearing in a video with different IDs, and has a wide range of applications in the fields of intelligent monitoring and autonomous driving. The current mainstream MOT methods are based on the tracking by detecting mode, and the tracking effect largely depends on the target detection method used. In some multi-target tracking competitions, the detector is fixed. After inputting video frames into the object detector [1; 2; 3], detection bounding boxes are generated, and the same target between two frames is associated with obtaining the trajectory of the target. At present, the widely used tracking methods first model the appearance of the detected targets, then calculate the affinity of the target features between frames, and finally match the targets through a matching algorithm. However, usually, appearance modeling will calculate the affinity between each trajectory and the global detection bounding boxes, which is prone to ID Switches. The introduction of motion prediction will make the tracking more robust. Sometimes, the detector recognizes the background as an object or the object is occluded, and a low-confidence detection bounding box, also known as a low-scoring box, appears. Due to its uncertainty, this type of box can easily disappear in a certain frame, resulting in tracking failure. In order to solve this situation, most of the current practice is to set the confidence threshold of the object detection network, and the detection boxes below this threshold will be filtered out. The problem with this approach is that, when the target is occluded, it cannot be associated with the previous frame. The matching problem of low-scoring boxes is crucial. In the MOT method of tracking by detecting mode, tracking and detection are two parts and do not affect each other. However, the tracking task is the main task, and thedetection task needs to serve the main tracking task. Although more and more one-stage MOT methods that integrate detection and tracking have been proposed, the accuracy is not optimal. In general work, NMS [4] in two-stage MOT is applied to the detection stage, first filtering low-scoring boxes, then removing redundant detection boxes and retaining the best one as the target box to match trajectories. However, while retaining the high-scoring frame, NMS may filter out the real low-scoring boxes due to occlusion, so that the filtered real frame cannot match the trajectory, resulting in tracking failure. Therefore, this paper improves the two-stage MOT method, and performs NMS in tracking instead of detection, which effectively avoids the boxes that need to be tracked from being filtered out. The current method is fair for high-scoring boxes and low-scoring boxes, and both directly use them together as target boxes to extract features. However, low-scoring boxes usually have smaller resolution and poor quality before feature extraction. In order to deal with the challenge of MOT, this paper proposes a MOT method that introduces bicubic interpolation. The low-scoring box has a high probability of being a distant target, its size is small, and the resolution is low after reshape. After interpolation, the resolution of the low-segment box can be improved, which is helpful for extracting its features. When calculating the affinity, the person ReID network is usually used to extract the target appearance information. Person ReID, as a task in computer vision, is trained on datasets dedicated to person ReID. Therefore, when applying person ReID to MOT, the domain gap caused by the different scopes has an impact on the matching results. Through unsupervised pre-training on the dataset, the obtained person ReID model effectively reduces the domain gap and has good performance on different datasets.