# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 2 Related Work
### Transformer in Tracking  
Transformers, originally developed for translation tasks in natural language processing (NLP) [8], have recently gained prominence in object tracking due to their exceptional global modeling capabilities. The pioneering work TransT [26] follows a two-stream architecture of the hybrid CNN-transformer [29], which constructs an attention-based feature fusion network by stacking a series of self-attention and cross-attention layers and employs a Siamese-like feature extraction backbone. Furthermore, DualTFR [30] constructs a Siamese-like dual-branch network based on pure transformers, which utilizes a series of transformer-based local global attention blocks for feature extraction and feature fusion. However, the two-stream tracker pipeline lacks the ability to perceive the target due to independent feature extraction. To address this limitation, MixFormer [27] proposes a one-stream pipeline that leverages the flexibility of the self-attention mechanism and introduces a mixed attention module (MAM) for simultaneous feature extraction and target information integration. Similarly, OSTrack [10] also develops a one-stream pipeline in which the template and search region are split, flattened, and linear projected, and then image embeddings are concatenated and fed into the layers of the encoder for joint feature learning and relationship modeling. The follow-up trackers [11; 12; 31] generally adopt a similar one-stream architecture. However, existing one-stream trackers allow for a free interaction between the template and all search images, which may lead to confusion about the target with the background in the case of insufficient discriminative power of the extracted template features. To this end, GRM [14] selects appropriate search region tokens to interact with template tokens, restricts unnecessary information flow between the template and search region tokens, and achieves a more flexible and effective modeling scheme. OIFTrack [32] deeply explored the information flow of tokens. By dividing the search tokens in the deeper encoder layers into target search tokens and non-target search tokens, OIFTrack achieves a bidirectional interaction from target search tokens to template tokens and prevents the interaction between non-target search tokens and template tokens,which improves the tracking accuracy. [PERSON] [33] et al. designed a Discriminative Distractors Mining (DDM) module for the search region, which enables the tracker to utilize the background for target localization more efficiently by refining the background prior knowledge and discarding meaningless background regions. SuperSBT [34] employs a hierarchical structure to improve shallow features with a local modeling layer, thereby enhancing the ability to distinguish the target from similar interfering objects during the feature extraction stage. The recently reported SeqTrack [35] and ARTrack [36] restrict the information flow between tokens in decoder layers to prevent tokens from attending to subsequent tokens. However, there is still no improvement to the encoder in these trackers. Conversely, we construct an encoder modeling scheme using the independent template stream for more accurate tracking.