LAUSR

Target tracking is one of the challenging tasks in computer vision. Usually, the center of target origins from the position with the largest response value, and the key to improving tracking performance is to learn reliable feature maps. This paper analyzes the characteristics of the tracking task, designs a global feature comparison function to extract the context, and proposes a feature supplement module based on the global comparison information for further performance improvement. In addition, we also design a template feature update module to supplement template features based on the search area features of the current frame to dynamically adjust model features, improve model generalization capabilities, and avoid model feature fixation. The proposed feature supplement model based on global feature comparison (FSGFC) is evaluated on five visual tracking benchmarks including OTB100, VOT2016, VOT2018, VOT2019 and UAV123. The experimental results show that the model obtains the state-of-the-art performance with a real-time speed.