A Global Re-Detection Method Based on Multi-Scale Feature Fusion and Attention Mechanism

To address the problem of target disappearance and reappearance in the filed of view frequently, a re-detection method is required to search for the target in the entire image. However, this search strategy will bring more interference from similar objects, and also makes it harder to detect small objects. Therefore, we designed a deep learning model, which is a global re-detection method based on multi-scale feature fusion and attention mechanisms. Firstly, the proposed method uses ResNet50 to extract shallow and deep features of the template and search region. Then, a multi-scale feature fusion module is used to fuse shallow and deep features, which enhance the accurate localization ability for small objects. Finally, A target-aware proposal enhancement module is proposed to enhance the response of proposal, especially the parts related with template, which filters out more accurate proposals and improves the ability of re-detection methods to handle interference from similar objects. The proposed method in this paper is carried on several datasets such as UAV20L, OTB, etc., and the experimental results show that the tracking performance of the proposed method is significantly improved.