###### Abstract
The target out-of-view in these challenges, which means that the object leaves the shot and reappears, is more likely to appear in videos than we imagine, making it harder for SOT models to be well applied to real life. Through our analysis, the main factors may be as follows:  
1. There is no uniform standard for videos, which are shot from different perspectives, and it is common for objects to move in and out of view. Videos, especially daily videos, are often casually shot, capturing scenes that are not the entire actual space but a limited fan-shaped area. At the same time, due to the ease of interaction between objects in videos and the uncertainty of their behavior, it is easy to cause objects to leave the shot. 2. The SOT algorithms are generally weak in the re-identification of objects out of shots [29]. Most SOT models focus on enhancing the performance of tracking by improving the ability of feature extraction and relation modeling [19]. However, when objects are obscured or change their appearance, the effective features of the object itself are extremely limited [30]. After the target leaves the shot, the model will choose the object with the highest confidence score in the current frame for long-term tracking, causing an inability to achieve target re-identification in a short period of time. Figure 2 shows the two possible reasons for poor tracking performance. In attempting to address the problem of poor tracking results of target out-of-view, this paper proposes a target re-identification method based on shot boundary object detection for single object tracking, called TRTrack. Specifically, we build a bipartite matching model  
Figure 2: Factors of unsatisfactory tracking results. (**a**) Targets’ frequently moving in and out of the shots. (**b**) The current SOT algorithms are generally weak in re-identification of the target after it leaves the shot. 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. Then, TRTrack carries out object detection at the shot boundary by improved YOLOv5-DeepSORT, in which the original loss function is substituted with the alpha-IoU. The backtracking identification module in TRTrack crops the detected objects and inputs them back into the tracking model as the search region image. Finally, the target with high confidence score will be re-identified. The contributions of this paper are shown below:  
* To deal with the problem of unsatisfactory tracking results resulting from target out-of-view, we propose a target re-identification method based on shot boundary object detection for single object tracking, called TRTrack;
* We build a bipartite matching model of candidate tracklets and neighbor tracklets optimized by the Hopcroft-Karp algorithm to judge the target leaves the shot and introduce the alpha-IoU loss function to YOLOv5-DeepSORT to enhance object detection capability;
* Through a wide range of experiments by self-built videos dataset CLV and benchmark dataset, TRTrack is verified to be applied well for target re-identification in most video tracking tasks. The rest of this paper is structured as follows: Section 2 presents the related work, including disappearing objects re-identification methods, a single object tracking model based on Transformer, and an object detection model following a top-down approach. Section 3 describes the proposed methodology of the target re-identification method TRrack with a preliminary object tracking module, boundary object detection module, and backtracking identification module. Section 4 shows the detailed experiment and results. Section 5 concludes our work.