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The main challenge of this task is the large intra-class and small inter-class variability of vehicles appearance, sometimes related with large viewpoint variations, illumination changes or different camera resolutions. To tackle these problems, we proposed a vehicle ReID system based on ensembling deep learning features and adding different post-processing techniques. In this paper, we improve that proposal by: incorporating large-scale synthetic datasets in the training step; performing an exhaustive ablation study showing and analyzing the influence of synthetic content in ReID datasets, in particular CityFlow-ReID and VeRi-776; and extending post-processing by including different approaches to the use of gallery video-clips of the target vehicles in the re-ranking step. Additionally, we present an evaluation framework in order to evaluate CityFlow-ReID: as this dataset has not public ground truth annotations, AI City Challenge provided an on-line evaluation service which is no more available; our evaluation framework allows researchers to keep on evaluating the performance of their systems in the CityFlow-ReID dataset.<\/jats:p>","DOI":"10.1007\/s11042-023-14511-0","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T02:45:29Z","timestamp":1679885129000},"page":"36815-36835","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Enhancing vehicle re-identification via synthetic training datasets and re-ranking based on video-clips information"],"prefix":"10.1007","volume":"82","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1248-0083","authenticated-orcid":false,"given":"Paula","family":"Moral","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"\u00c1lvaro","family":"Garc\u00eda-Mart\u00edn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 M.","family":"Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jes\u00fas","family":"Besc\u00f3s","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,21]]},"reference":[{"issue":"2","key":"14511_CR1","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3390\/ijgi11020085","volume":"11","author":"KLM Ang","year":"2022","unstructured":"Ang KLM, Seng JKP, Ngharamike E, Ijemaru GK (2022) Emerging technologies for smart cities\u2019 transportation: geo-information, data analytics and machine learning approaches. 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