###### Abstract  
With the advantages of simple model structure and performance-speed balance, the single object tracking (SOT) model based on a Transformer has become a hot topic in the current object tracking field. However, the tracking errors caused by the target leaving the shot, namely the target out-of-view, are more likely to occur in videos than we imagine. To address this issue, we proposed a target re-identification method for SOT called TRTrack. First, we built a bipartite matching model of candidate tracklets and neighbor tracklets optimized by the Hopcroft-Karp algorithm, which is used for preliminary tracking and judging the target leaves the shot. It achieves 76.3% mAO on the tracking benchmark Generic Object Tracking-10k (GOT-10k). Then, we introduced the alpha-IoU loss function in YOLOv5-DeepSORT to detect the shot boundary objects and attained 38.62% mAP\({}_{75.95}\) on Microsoft Common Objects in Context 2017 (MS COCO 2017). Eventually, we designed a backtracking identification module in TRTrack to re-identify the target. Experimental results confirmed the effectiveness of our method, which is superior to most of the state-of-the-art models. target re-identification; single object tracking; object detection; YOLO; DeepSORT 2023  
long-term object detection [20]. Current object detectors based on deep learning have become mainstream models, mainly divided into two-stage detectors and single-stage detectors. The former uses a proposal mechanism to obtain image regions that are more likely to contain objects in order to achieve target prediction, mainly including R-CNN [21], Fast R-CNN [22], and Faster R-CNN [23]. The latter directly detects objects from anchors after extracting input image features, mainly including SSD [24], YOLO series [25], and DETR [26]. For example, the detectors of the YOLO series directly pre-define anchors with multiple scales at all locations of the image and predict the category probability and confidence score, greatly improving the speed of object detection [27]. However, there are some challenges that have been difficult to solve for SOT models, mainly including target occlusion, out-of-view, deformation, and scale variation. These challenges can easily result in an inability to continue tracking or result in tracking errors. Figure 1 shows some examples in the tracking benchmark dataset GOT-10k [28]. In fact, not only in SOT but also in other tasks of computer vision, such as multi-object tracking (MOT), object detection, and person re-identification (Re-ID), these four major challenges are urgent issues to be addressed. The specific descriptions of these challenges are as follows:  
1. Target occlusion. Due to the complex environment in videos, when the tracked targets are moving or when other objects in the shots are moving, the tracked targets may be obscured by other objects for a period of time. In Figure 1, the moving ship is obstructed by the railing in front of the camera. 2. Target out-of-view. This situation often occurs in videos where the tracked targets are moving rapidly, regardless of whether the shots are fixed or moving. Because the targets are generally not aware of the range of the shots, as they move, it may cause them to leave the cameras. The monkey's rapid movement causes it to leave the shot in Figure 1.
3. Target deformation. Tracked targets with irregular shapes and sizes or actively changing their own appearance can cause this situation, especially when they move. In  
Figure 1: Example of SOT challenges in benchmark dataset GOT-10k. Figure 1, the small boat changes its plane shape in the shots due to its rotational motion. 4. Target scale variation. This situation occurs when the tracked targets approach or move away from the shots. The small dog's running back and forth in front of the camera changes its scale within the lens in Figure 1.  
Even SOT models based on Transformers find it difficult to cope with these challenges, resulting in tracking errors.