# Transformer-Based Visual Object Tracking with Global Feature Enhancement
## 1 Introduction  
Single-object tracking is pivotal in computer vision [1; 2; 3] and is tasked with estimating an object's position and motion trajectory across frames. Overcoming challenges like size variations, occlusions, and deformations are persistent issues [4; 5]. In the field of computer vision, advancements have been made in CNN-based tracking algorithms, such as the Siamese networks [6; 7; 8], particularly in addressing challenges like size variations, occlusions, and deformations. SiamFC [6] is a classic example that employs cross-correlation operations to compute a score map, determining the target's bounding box by convolving the feature maps of the template and search regions. However, this approach, due to significant differences between the score map and the feature map, overlooks the semantic information of the target, hindering the subsequent classification and regression operations. To solve this problem, many transformer-based trackers [9; 10; 11; 12] are used for feature fusion. Mixformer [10] proposes a mixed attention module (MAM), which applies the attention mechanism to simultaneously extract features and exchangeinformation. OSTrack [11] integrates the target information more widely into the search area to better capture their correlation. In [12], the learning and memory capabilities of the transformer are utilized by encoding the information required for tracking into multiple tokens. Through cross-attention operations in the cross-feature augment (CFA) module, TransT [9] obtains fused features containing rich semantic information from templates and search regions. This fusion feature can be directly used for the subsequent classification and regression operations to estimate the state of the target. By introducing transformer operations [13], our proposed GFETrack replaces the cross-correlation operations adopted in TransT. Although the attention score map of TransT may exhibit some dispersion, as shown in Figure 1a, the proposed GFETrack exhibits a more concentrated pattern, as shown in Figure 1b. Considering the limitations of TransT's CFA module in extracting target features and position information, the proposed GFETrack with global feature enhancement can focus more on the object itself and make it more suitable for the subsequent regression operations. With the rise of Vision Transformer [14; 15], self-attention techniques show promise in visual tasks. However, their quadratic computational complexity has prompted research into mitigating this challenge. Sparse global attention patterns [16; 17] and smaller attention windows [18; 19] can reduce costs, but at the risk of overlooking information or sacrificing long-term dependency modeling. In Fast re-OBJ [20], the authors efficiently leverage the intermediate outputs of the instance segmentation backbone (ISB) for triplet-based training, avoiding redundant feature extraction during both ISB and the embedding generation module (EGM). In contrast, our method employs a distinct strategy. Initially, we pass the \(Q\) and \(K\) matrices through a \(\varphi\) map to create new matrices \(\tilde{Q}\) and \(\tilde{K}\), ensuring that the attention matrix \(A\) is equal to or approximately equal to the product of \(\tilde{Q}\) and \(\tilde{K}\). This approach offers computational advantages by first calculating the product of \(\tilde{K}\) and V after decomposing matrix \(A\), followed by multiplying the result by \(\tilde{Q}\), reducing the spatial complexity of regular attention from \(O(N^{2}+Nd)\) to \(O(Nr+Nd+rd)\). The main contributions of this paper are as follows:  
* An enhanced cross-attention module is proposed to boost object region feature information in cross-attention. It is primarily achieved by computing the average attention weights and giving higher weights to the highly ranked positions to enhance matching accuracy. Figure 1: The attention heat maps of different feature fusion layers. (**a**) The attention heat maps obtained by TransT [9]. (**b**) The attention heat maps obtained by the proposed GFETrack.