## 2 Related Work  
In recent years, some scholars have tried to improve the robustness of tracking objects through deep learning methods. They increased the number of training sets to improve the ability of feature extraction or obtain a more general tracker. Some scholars use filters to compare the partially extracted object features and then dynamically update the filter. Many scholars have proposed methods trying to solve the object encountered motion blur in the tracking. In 2021, [PERSON] et al. proposed a new generic scheme based on the generative adversarial network (GAN). They used a fine-tune discriminator as an adaptive blur evaluator to enable selective frame de-blurring during tracking to improve the robustness of tracking blurred objects [13]. In 2021, [PERSON] et al. cited image quality assessment (IQA) and de-blurring components into the basic D3S (a discriminative single-shot segmentation tracker) framework to enhance context patches, thereby improving the accuracy of tracking blurred objects [14]. In 2021, [PERSON] et al. used the circle algorithm to calculate the neighborhood of offset estimation and then used a short-learning based on SiamRPN++ to achieve online tracking of high-speed targets to solve the impact of motion blur [15]. In 2021, [PERSON] et al. proposed a space-time memory planar object tracking networks (STMPOT) network to classify the pixels belonging to the object or background of the current frame by remembering the information of the object and background of each frame to solve the motion blur effects [16]. In 2022, [PERSON] et al. built a new one-shot MOT tracker. They used a global embedding search to propagate previous trajectories to the current frame and extended the role of ID embedding from data association to motion prediction which improves the tracker only relying on single-frame detections to predict candidate bounding boxes to address the impact of motion blur [17]. In 2022, [PERSON] et al. proposed a novel JDT model that recovers missed detections by learning object-level spatiotemporal coherence from edge features in graph neural networks (GNNs), while correlating detection candidates for consecutive frames, solving the effect of motion blur [18]. Many scholars have proposed methods trying to solve object encountered deformation in the tracking. In 2019, [PERSON] et al. proposed a deformable convolutional layer. It can enrich object appearance representations in a detection tracking framework to adaptively enhance its original features. They believe that the rich feature representation through deformable convolution can help the convolutional neural network (CNN) classifier to distinguish the target object from the background and reduce the influence of deformation on tracking objects [19]. In 2019, [PERSON] et al. proposed an improved algorithm that improved the feature extraction ability by incorporating the multi-domain training, redesigned both the selection criteria for optimal action and the reward function, and used an effective online adaptive update strategy to adapt to the deformation of the object during tracking [20]. In 2020, [PERSON] et al. proposed to use offline pre-trained Resnet-101 to obtain mid-level and high-level extracted features combined with correlation filters to improve the ability to track moving objects with deformation [21]. In 2021, [PERSON] et al. proposed a new approach by embedding the occlusion perception block into the model update stage to adaptively adjust the model update according to the situation of occlusion and using the relatively stable color statistics to deal with the appearance shape changes in large targets and compute the histogram response scores as a complementary part of final correlation response to mitigate appearance deformation [22]. In 2022, [PERSON] et al. proposed a novel deformed sample generator to obtain a more general classifier and avoid larger training datasets. The classifier and the deformed sample generator are learned jointly, thereby improving the robustness of tracking deformed objects [23].