# FETrack: Feature-Enhanced Transformer Network for Visual Object Tracking
## 1 Introduction
Meanwhile, the loss function of each head network introduces additional hyperparameters, which need to be trained and inferred separately, inevitably increasing computational resources. This is contrary to a simple and efficient end-to-end framework required in real-world applications. To address the above issues, we propose a feature-enhanced transformer-based network for robust visual object tracking (FETrack). The proposed FETrack employs a transformer-based encoder-decoder structure, where the encoder is responsible for extracting visual features of the template and the search region and the decoder is reliable in predicting the target bounding box autoregressively. To prevent the templates from being altered by the background noise, we embed an independent template stream in the original one-stream encoder, in which we employ the self-attention mechanism to enhance the learned template features and alleviate the interference from the search region. Additionally, we adopt cross-attention to enhance both the template and search region features to extract target-oriented features under mutual guidance. Then, we propose a template-filtering approach to obtain a small yet more accurate template cropped from the template image to improve tracking accuracy and reduce computational efforts. Subsequently, to simplify the complexity of the prediction network, we adopt the sequence-learning-based method that converts four values of the target bounding box into discrete token sequences in the decoder. Specifically, a causal transformer is used to autoregressively generate the sequences of the bounding box value one by one and further fuse them with visual features from the encoder with a cross-attention layer. As a result, the proposed FETrack prevents both customized head networks and complicated post-processing techniques. Additionally, during inference, different from existing trackers that rely on the two-stage inference update or the two-stage training network, we design a novel online template-updating strategy. This strategy calculates the dynamic threshold adaptively to obtain the high-quality template, so as to prevent incorrect template updating due to long-term occlusion and the disappearance of the target. We evaluate the performance of our tracker on six benchmarks (i.e., GOT-10k [17], LaSOT [18], TrackingNet [19], LaSOT\({}_{ext}\)[20], OTB100 [21], and NFS30 [22]) and experimental results show that our FETrack achieves the state-of-the-art performance. In summary, the main contributions of this paper are as follows:  
* To tackle the performance degradation of one-stream trackers due to unrestrained information interaction, we propose FETrack, a feature-enhanced transformer-based network for visual object tracking. Experimental results on six benchmarks validate the superiority of our FETrack and the effectiveness of the proposed modules. * An independent template stream is incorporated into the one-stream encoder, which effectively prevents the template feature quality from background interferences. * A dynamic threshold-based online template-updating strategy is presented to adaptively select the template with the highest similarity, and a template-filtering approach is designed to alleviate the background noise and substantially reduce the computational cost.