# Double Branch Attention Block for Discriminative Representation of Siamese Trackers
## 1 Introduction  
Generic object tracking is a fundamental task in the field of computer vision, with a wide range of application needs in the fields of monitoring, automatic driving [1, 2], surgical detection [3], posture recognition [4], and industrial measurement [5]. In recent years, many excellent achievements emerged in the visual object tracking task, but this task remains challenging due to the impact of external factors, such as target deformation, environmental illumination, and background disturbance. These adverse factors damage the feature representation of objects, making it difficult for trackers to distinguish the target from the background distractors, resulting in misdetection. To enhance the feature representation of objects, the majority of current tracking algorithms rely on the neural network to obtain fine target features [6, 7, 8]. Benefiting from the strong information extraction capability of the neural network, visual object tracking has made rapid progress in recent years. Among them, the Siamese framework proposed by [PERSON] [9] in 2016 has achieved a good balance between accuracy and efficiency. It uses the same set of network parameters to extract the deep features of a given target and search inputs, then locates the target by calculating the cross-correlation similarity between the two. The core of this framework is to transform the tracking problem into the template matching problem. Therefore, the template is not restricted by the training data, making the Siamese framework universal in object tracking. However, the background distractors bring side effects to the discriminative representation of the target, and the effects are hard to suppress. It is because the Siamese framework follows the principle that the target within the search region shows substantial feature similarity to the given target, whilethe background shows weak feature similarity. Some background distractors may also produce features similar to the given target, thus leading the tracker to judge them as targets. Fortunately, these background distractors produce significant differences from the given target in some detail features. It would be easier to distinguish the target from distractors by performing similarity calculations for features that produce differences. However, the changeable tracking scenarios are challenging to determine which feature can produce differences in the current tracking scenario. To obtain these features and add them to the calculation of distinguishing target from background distractors, we need to expand the range of feature representation. Many strategies have been used to obtain a rich feature representation in the Siamese framework. Some algorithms increased the amount of input information. DSiam [10] processed the characteristics of a given target, the current search image, and the previous search image in one prediction by integrating the historical target features to build a rich target template information database. It had a good treatment of the deformation problem, but the distractor problem remains. DaSiamRPN [11] added negative samples containing distractors to the training data, facilitating network learning about handling similar negative samples, but it required a large amount of additional training data from other domains. Other algorithms obtained a richer feature representation by changing the network structure. SiamRPN [12] extracted the features of the search regions of different scales and shapes through the region proposal network (RPN) and indirectly obtained the target shape information through the similarity calculation. Similar to DSiam, it only has a good effect on the target deformation problem. In addition, SiamDW [13] and SiamRCR [14] enriched feature representation by refining the current features. SiamDW obtained deeper pattern information to fit complex tracking objects by increasing the depth of the network. SiamRCR added a regression branch to adjust for features dynamically against the samples.