# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 1 Introduction  
As a crucial task in computer vision, visual object tracking (VOT) [1] has been widely applied in video surveillance [2], autonomous driving [3], human-computer interaction [4], medical imaging [5], etc. VOT aims to estimate the state (i.e., position and scale) of the target across subsequent frames of a video sequence by constructing an appearance model based on the target provided in the first frame. Despite numerous excellent VOT methods proposed in the past decades, designing a robust and accurate tracker still faces significant challenges due to various complex real-world scenarios such as appearance change, occlusion, background clutter, and similar objects. Benefiting from the powerful feature representation capabilities of Convolutional Neural Networks (CNNs) and transformers, numerous deep-learning-based trackers have achieved remarkable success. Especially, transformer-based trackers [6] have shown an impressive performance in dealing with challenging real-world scenarios. Classic tracking paradigms (e.g., Siamese network and two-stream pipeline trackers) can be roughly divided into three stages, i.e., feature extraction, feature fusion, and target prediction. In general, robust trackers possess powerful feature extraction and accurate fusion capabilities [7]. Although CNN-based trackers have achieved remarkable results, they suffer from weak global interaction capabilities and tend to obtain only local optimal solutions due to the limitation by the local feature extraction of CNNs. In recent years, benefiting from the advantages of capturing contextual information, handing long sequential data, and parallel computing, transformers have shown great potential in the field of natural language processing (NLP) [8]. Inspired by this, many efforts have been devotedto developing transformer architecture to construct tracking networks, thereby improving the global interaction capabilities. Specifically, benefiting from the powerful global feature modeling, transformers have been gradually applied in the feature extraction and feature fusion stages of tracking networks. At present, most of the mainstream transformer-based trackers [9; 10; 11; 12] adopt the one-stream one-stage tracking pipeline. Such trackers leverage the flexibility of the attention mechanism and unify feature extraction and interaction between the template and the search region into a Vision Transformer (ViT) [13] backbone. By performing extensive feature matches between the template and the search region at multiple levels, the one-stream tracker acquires discriminative features due to its ability to suppress redundant non-target features. Nevertheless, there are often significant scale gaps between the target and the background in the search region [14; 15], where interfering objects with a similar appearance to the target may be present. As a result, the deep interaction between the template and the search region may introduce the background noise into template features. This degrades the template feature quality and hinders the tracking performance, especially for trackers relying severely on similarity matching. On the other hand, the undesirable interaction between the template and the search region may cause enhanced features of similar interfering objects to contain target features from the template, resulting in confusion between the target and the background during subsequent target prediction. Especially in the case where there are numerous similar objects surrounding the target, such similar objects impair the awareness ability of the tracker [16], leading to tracking drift or even loss of the target. Additionally, most existing trackers decompose the prediction process into a series of subtasks, such as foreground-background classification, scale estimation, and center point location. These subtasks usually require specific head networks and various post-processing techniques, which tend to complicate the tracking framework and decrease the speed of training and inference.