Abstract
Video synthetic aperture radar (ViSAR) system presents significant potential for moving target detection and surveillance of dynamic region of interests. In this paper, a novel moving target shadow detection and tracking approach for ViSAR is proposed based on deep neural network (DNN) and multi-object tracking (MOT) algorithm. The faster region-based convolutional neural network (FrRCNN) with feature pyramid networks (FPN) is exploited for shadow detection in a single frame. Then, an online MOT approach is employed for tracking the shadows and improving the detection performance. Finally, experiments on measured data have demonstrated that the proposed approach processes an accepted tracking performance.
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