The lack of traffic data is a bottleneck restricting the development of Intelligent Transportation Systems (ITS). Most existing traffic data completion methods aim at low-dimensional data, which cannot cope with high-dimensional video data. Therefore, this paper proposes a traffic data complete generation adversarial network (TDC-GAN) model to solve the problem of missing frames in traffic video. Based on the Feature Pyramid Network (FPN), we designed a multiscale semantic information extraction model, which employs a convolution mechanism to mine informative features from high-dimensional data. Moreover, by constructing a discriminator model with global and local branch networks, the temporal and spatial information are captured to ensure the time-space consistency of consecutive frames. Finally, the TDC-GAN model performs single-frame and multiframe completion experiments on the Caltech pedestrian dataset and KITTI dataset. The results show that the proposed model can complete the corresponding missing frames in the video sequences and achieve a good performance in quantitative comparative analysis.
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