  This work focuses on multi-target tracking in Video synthetic aperture radar.
Specifically, we refer to tracking based on targets' shadows. Current methods
have limited accuracy as they fail to consider shadows' characteristics and
surroundings fully. Shades are low-scattering and varied, resulting in missed
tracking. Surroundings can cause interferences, resulting in false tracking. To
solve these, we propose a shadow-oriented multi-target tracking method
(SOTrack). To avoid false tracking, a pre-processing module is proposed to
enhance shadows from surroundings, thus reducing their interferences. To avoid
missed tracking, a detection method based on deep learning is designed to
thoroughly learn shadows' features, thus increasing the accurate estimation.
And further, a recall module is designed to recall missed shadows. We conduct
experiments on measured data. Results demonstrate that, compared with other
methods, SOTrack achieves much higher performance in tracking accuracy-18.4%.
And ablation study confirms the effectiveness of the proposed modules.

Image and Video Processing