# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 2 Related Work  
In recent years, convolutional networks have been widely used in computer vision tasks [21], including object detection, segmentation, and tracking. SiamFC [6] was the first to apply Siamese networks for visual object tracking, which predicts the object states by calculating the distance between the current frame and the template. SiamFC++ [8] is an improved version of SiamFC that introduces a new network architecture and training method, enabling object tracking in different scenarios. SiamMask [22] uses Siamese networks for both object tracking and segmentation simultaneously. SA-Sia [23] utilizes feature mapping to extract spatial information leading to more accurate and robust object tracking. SAT [7] employs a deep Siamese network to assess the similarity between objects in each frame, offering a novel solution for evaluating and addressing the association challenges in consecutive frames. However, the local matching strategy, which is based on cross-correlation, could lead to sub-optimal results, especially when the object is occluded or partially visible. Moreover, semantic information about the object may be lost during the correlation operation, resulting in imprecise object boundaries. Thus, an improved transformer and attention mechanism are proposed to replace traditional correlation operations in object tracking. It can effectively extract global contextual information while preserving an object's semantic information, resulting in more robust and accurate tracking results. The transformer is a popular neural network architecture that is widely used in natural language processing (NLP) tasks [13]. It consists of attention-based encoders and decoders. Self-attention, which is the main module of a transformer, can compute representations of input sequences. It allows each position in the input sequence to focus on the other positions and calculate the weighted averages of their values. DETR [24] and ViT [14] are early methods for introducing transformer models into the field of computer vision. In visual object tracking fields, transformer-based tracking methods achieve significant improvements compared to Siamese network-based trackers. The correlation operation in the Siamese network is replaced by a self-attention module from the transformer in TransT [9] to fuse the information between the template and search region. [PERSON] [25] applies an encoder-decoder structure in tracking, where the encoder models the global spatiotemporal feature dependencies between the object and search region, while the decoder learns embedded queries to predict the spatial location of the object. SwinTrack [26] introduced a fully attention-based transformer algorithm for feature extraction and feature fusion, enabling the complete interaction between an object and a search region in the tracking process. The encoder-decoder framework based on transformers is widely applied to sequence prediction tasks, as self-attention models the interaction between elements in the sequence. Based on these works, we find that traditional transformer methods struggle to distinguish between the object to be tracked and similar interfering objects. To enhance the object information within the fused features, the ECA module is proposed to boost the attention on tracking the object. The ECA module can calculate the average attention level for each position in the fused feature sequence and weigh the positions with higher attention levels. This enhances the feature information in the object region and improves matching accuracy. In recent years, a series of new attention mechanisms, such as fast attention [27], have garnered research interest. Sparse attention [28] reduces computational costs by constraining attention weights to consider only relationships within a neighborhood. [PERSON] et al. [29]proposed an attention mechanism to embed relative positional information into attention calculations, allowing the model to better handle dependencies in long sequences.