Abstract
Video synthetic aperture radar (Video-SAR) allows continuous and intuitive observation and is widely used for radar moving target tracking. The shadow of a moving target has the characteristics of stable scattering and no location shift, making moving target tracking using shadows a hot topic. However, the existing techniques mainly rely on the appearance of targets, which is impractical and costly, especially for tracking targets of interest (TOIs) with high diversity and arbitrariness. Therefore, to solve this problem, we propose a novel guided anchor Siamese network (GASN) dedicated to arbitrary TOI tracking in Video-SAR. First, GASN searches for matching areas in the subsequent frames with the initial area of the TOI in the first frame are conducted, returning the most similar area using a matching function, which is learned from general training without TOI-related data. With the learned matching function, GASN can be used to track arbitrary TOIs. Moreover, we also constructed a guided anchor subnetwork, referred to as GA-SubNet, which employs the prior information of the first frame and generates sparse anchors of the same shape as the TOIs. The number of unnecessary anchors is therefore reduced to suppress false alarms. Our method was evaluated on simulated and real Video-SAR data. The experimental results demonstrated that GASN outperforms state-of-the-art methods, including two types of traditional tracking methods (MOSSE and KCF) and two types of modern deep learning techniques (Siamese-FC and Siamese-RPN). We also conducted an ablation experiment to demonstrate the effectiveness of GA-SubNet.
Highlights
Video synthetic aperture radar (Video-SAR) provides high-resolution SAR images at a faster frame rate, which is conducive to the continuous and intuitive observation of ground moving targets
Since the Sandia National Laboratory (SNL) of the United States first obtained high-resolution SAR images in 2003 [2], many scholars have investigated the problem of moving target tracking in Video-SAR [3,4,5,6,7]
guided anchor Siamese network (GASN) is based on the idea of similarity learning, which compares a template image z to a search image x and returns a high score if the two images depict the same target
Summary
Video synthetic aperture radar (Video-SAR) provides high-resolution SAR images at a faster frame rate, which is conducive to the continuous and intuitive observation of ground moving targets. Due to this advantage, Video-SAR brings about important applications in SAR moving target tracking [1]. Since the Sandia National Laboratory (SNL) of the United States first obtained high-resolution SAR images in 2003 [2], many scholars have investigated the problem of moving target tracking in Video-SAR [3,4,5,6,7]. Due to different angles of illumination, the scattering characteristics of moving targets change with the movement of the platform. It is difficult to track a moving target directly because the imaging results of the moving target usually shift from their true position.
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