# Visual Tracking with FPN Based on Transformer and Response Map Enhancement
## 1 Introduction  
As an important computer vision task, object tracking is widely applied in the fields of UAV application [1], industrial production and manufacturing [2], automatic driving [3] and so on. Specifically, object tracking refers to the process of estimating the visual target trajectory when only an initial state of the target (in a video frame) is available. However, state-of-the-art visual trackers are still unable to handle serious real-world challenges such as severe variations in target appearance, similar object and occlusion. These challenges can reduce the accuracy and robustness of tracking, which may lead to tracking failures. The mainstream models can be classified into correlation filter-based trackers and Siamese-based trackers. The main purpose of the correlation filter is to estimate the optimal image filter, which can maximize its response to the region of the target when the search area is entered; however, the accuracy and robustness of the tracking task still needs to be improved. Recently, due to the superior feature extraction ability of deep learning, the accuracy of object tracking has made great progress and Siamese network-based trackers have attracted extensive attention in the field of visual tracking. Siamese-based networks are composed of two independent feature extraction networks. Given the pairs of target and search regions, these two-stream networks compute the same function to produce a similarity map. Then, the search region with the highest similarity to the target template is found by matching the response map to complete the tracking. In early Siamese-based trackers such as SINT [4], the Siamese network was trained to search for the candidate region most similar to the initial object appearance. The recent SiamFC [5] introduces the cross-correlation layer as a fusion tensor and highly improves the accuracy. However, SiamFC lacks a bounding box regression and is required to carry out a multi-scale test, which makes it less elegant. SiamRPN [6] adds the region proposalextraction sub-network to the Siamese network. By training both the classification branch and regression branch for visual tracking, SiamRPN avoids the time-consuming step of extracting multi-scale feature maps for the object scale invariance. However, the feature expression ability of SiamRPN is still limited due to the feature extraction ability of the shallow network. SiamRPN++ [7] introduced a deeper network, such as ResNet-50, by improving the sampling method [8], but it also finds it difficult to deal with similar object and target occlusion using only the deep feature. In deep learning, low-level features can better represent spatial information such as color, texture and edge and corner points, which is conducive to target location, the determination of boundary box size and aspect ratio, while high-level features pay more attention to semantic information such as categories and attributes, which is more conducive to foreground and background differentiation. The effective fusion of high-level and low-level features can improve tracking accuracy and robustness. In view of the above, this paper proposes a target tracking algorithm with a response map enhancement module based on the Transformer feature pyramid (TR-Siam). As shown in Figure 1, the framework mainly consists of four simple sub-networks: feature extraction sub-network, feature fusion sub-network, similarity match sub-network and prediction head. The Transformer-based feature pyramid (TFN) is a key feature of the feature fusion sub-network. TFN can efficiently fuse shallow and deep features to achieve feature enhancement. In addition, the response map enhancement module is introduced into the similarity match sub-network to improve the algorithm's foreground and background recognition abilities. Experiments show that the proposed TR-Siam has better robustness in dealing with complex scenes such as similar object, occlusion and viewpoint change. Our main contributions are:  
1.