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Most existing traffic data completion methods aim at low\u2010dimensional data, which cannot cope with high\u2010dimensional video data. Therefore, this paper proposes a traffic data complete generation adversarial network (TDC\u2010GAN) 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\u2010dimensional data. Moreover, by constructing a discriminator model with global and local branch networks, the temporal and spatial information are captured to ensure the time\u2010space consistency of consecutive frames. Finally, the TDC\u2010GAN model performs single\u2010frame 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.<\/jats:p>","DOI":"10.1155\/2021\/8898681","type":"journal-article","created":{"date-parts":[[2021,3,15]],"date-time":"2021-03-15T22:07:52Z","timestamp":1615846072000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A High\u2010Dimensional Video Sequence Completion Method with Traffic Data Completion Generative Adversarial Networks"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2497-6556","authenticated-orcid":false,"given":"Lan","family":"Wu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tian","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglin","family":"Wen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunpeng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fanshi","family":"Kong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,3,15]]},"reference":[{"key":"e_1_2_9_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2016.09.015"},{"key":"e_1_2_9_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2909571"},{"key":"e_1_2_9_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2018.11.003"},{"key":"e_1_2_9_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2019.09.019"},{"key":"e_1_2_9_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/tim.2012.2214952"},{"key":"e_1_2_9_6_2","doi-asserted-by":"publisher","DOI":"10.3390\/s150715443"},{"key":"e_1_2_9_7_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.05848"},{"key":"e_1_2_9_8_2","first-page":"1","article-title":"Blockchain-based systems and applications: a survey","volume":"21","author":"Zhang J.","year":"2020","journal-title":"Journal of Internet Technology"},{"key":"e_1_2_9_9_2","doi-asserted-by":"publisher","DOI":"10.32604\/cmc.2019.06497"},{"key":"e_1_2_9_10_2","doi-asserted-by":"publisher","DOI":"10.31209\/2019.100000095"},{"key":"e_1_2_9_11_2","doi-asserted-by":"publisher","DOI":"10.3390\/s16070982"},{"key":"e_1_2_9_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.trc.2017.11.027"},{"key":"e_1_2_9_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2018.08.067"},{"key":"e_1_2_9_14_2","doi-asserted-by":"publisher","DOI":"10.1111\/mice.12595"},{"volume-title":"Research on travel time prediction model of freeway based on gradient boosting decision tree","year":"2018","author":"Cheng J.","key":"e_1_2_9_15_2"},{"volume-title":"Vehicle trajectory prediction by integrating physics-and maneuver-based approaches using interactive multiple models","year":"2017","author":"Xie G.","key":"e_1_2_9_16_2"},{"key":"e_1_2_9_17_2","unstructured":"GoodfellowI. 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