Shadow-Assisted Moving Target Tracking Based on Multidiscriminant Correlation Filters Network in Video SAR

Abstract

Moving targets always defocus and shift outside the scene in video synthetic aperture radar (video SAR) image sequences. However, the shadows of moving targets are immune to these issues and can reveal the true position of the moving targets. As such, by tracking the shadows of moving targets in the video SAR image sequence, it becomes feasible to keep track of these targets. Nevertheless, due to the small pixel size and time-varying characteristics of the target shadow, current prevailing tracking methods often prove insufficient for direct tracking of the shadow. In this paper, a shadow-assisted tracking method for moving targets based on multilevel discriminant correlation filters network (MDCFnet) is proposed. Primarily, we designed a Reverse Feature Pyramid Network (RFPN) that integrates multiple high-level features into low-level features to obtain multiple features with higher distinguishability and resolution, thereby enhancing the final tracking accuracy and precision. Furthermore, we devised Multi-level Discriminative Correlation Filters (MDCF) to perform filtering tracking under multiple feature maps. Real dataset processing results are provided to demonstrate that the proposed method outperforms other state-of-the-art methods.