# Fast and Accurate Visual Tracking with Group Convolution and Pixel-Level Correlation
## 1 Introduction  
As one of the research contents in computer vision, visual object tracking has wide application prospects and value in security surveillance, intelligent transportation, autonomous driving, human-computer interaction, autonomous robotics, marine exploration, military target identification, and tracking. Visual object tracking was first carried out using correlation filtering for tracking, and with the development of deep learning, convolutional neural networks have gradually been widely used due to their powerful feature extraction capabilities. Visual object tracking is usually divided into three parts: using a backbone network to extract the target's features, then correlating the template features with the search, and finally utilizing a classification and regression sub-network to predict the center and bounding box of the target. Siamese networks are widely used in object tracking with this structure. SiamFC [1] first introduced Siamese networks to object tracking. In SiamFC, the template features are correlated with the search features to find the region with the largest response and complete tracking and evaluation. Since then, many works have been carried out on Siamese networks in object tracking. SiamRPN [2] introduced the RPN (region proposal network) structure of object detection to tracking, constructing two branches--one for the regression of the target bounding box, and the other for the classification of the target--where the multi-scale anchor box improves the performance under object scale changes. SiamRPN++ [3] solved the problem of poor results in deep networks due to the destruction of translation invariance when the network is deepened, successfully using ResNet [4] and MobileNet [5] as the backbone networks. SiamFC++ [6] removes the anchorframe and changes the output prediction to an anchor-free style without presetting the anchor frame. In recent years, transformer structures have boomed in various fields of computer vision. TransT [7] uses the structure of a transformer as the correlation operation, which improves accuracy. [PERSON] et al. [8] used a transformer structure as the backbone network and utilized a decoder to reconstruct the target appearance within the search region so that the template is close to the search frame, rather than the search frame being directly related to the template image. In this way, the robustness of the tracker is enhanced, even if the appearance of the target has changed. [PERSON] et al. [9] proposed a one-and-a-half-stream structure that uses an adaptive token division method so that the search and template regions have self-attention and cross-attention, as in a two-stream structure, as well as advanced template interactions with the search region, as in a one-stream structure. This structure outperforms some two-stream and one-stream pipelines. In object tracking, training datasets usually contain many videos and multiple forms of motion. Some annotations may be less accurate due to occlusion and present similarities; thus, some trackers use data processing methods to improve the performance. [PERSON] et al. [10] analyzed the dataset distribution in a low-level feature space and proposed a sample squeezing method to eliminate redundant samples, making the dataset more abundant and informative and increasing the diversity of the dataset. [PERSON] et al. [11] adaptively obtained a tight enclosing box; when the target is in deformation or rotation, the bounding box cannot tightly enclose the target. They also designed a classifier to determine whether the target is occluded or not, which helps to avoid the collection of occluded samples for tracker updates, and to improve accuracy. However, there are still some challenges in practical applications. Target appearance changes, illumination variation, and occlusion can affect the effectiveness of tracking. Generally, different features of the object are extracted in different stages of the network.