Abstract
Aiming at the problem of missing traffic video frames, this paper proposes a complementary model of generative adversarial networks. The model uses the Feature Pyramid Network (FPN) to obtain feature maps of multiple scales on the input video frame. By fusing feature maps of different scales, it can better integrate the semantic information on the frame. The local patch discriminator added to the discriminator model effectively ensures the accuracy and continuity of the completed frame. Experimental results on Caltech pedestrian dataset and KITTI dataset show the good performance of the proposed model.KeywordsGenerative adversarial networksFeature Pyramid NetworkSemantic information
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