Abstract
Shadows of ground moving targets in video synthetic aperture radar (SAR) has been found very useful in ground moving target indication (GMTI) for they can indicate the real positions of moving targets at different times, which is significant for SAR reconnaissance and surveillance. However, nearly all the shadow-based SAR GMTI methods only focused on detecting shadows in every separate frame and failed to make full use of the continuous observation ability of video SAR. In this paper, we propose to apply a deep learning-based multi-target tracking method to solve this problem and find that the FairMOT network which jointly detects and re-identifies objects in sequential frames is suitable for this task. To verify its performance, video SAR datasets that contain shadows of ground moving targets are obtained by simulation. The experiments on the simulation datasets show that the introduced network in this work can achieve a state-of-the-art result, for instance, the multiple object tracking accuracy (MOTA) can reach 83.4%.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
