{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,12]],"date-time":"2026-09-12T13:55:35Z","timestamp":1789221335013,"version":"build-2803163510"},"reference-count":33,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,12]],"date-time":"2023-08-12T00:00:00Z","timestamp":1691798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In recent years, the research on object detection and tracking is becoming important for the development of advanced driving assistance systems (ADASs) and connected autonomous vehicles (CAVs) aiming to improve safety for all road users involved. Intersections, especially in urban scenarios, represent the portion of the road where the most relevant accidents take place; therefore, this work proposes an I2V warning system able to detect and track vehicles occupying the intersection and representing an obstacle for other incoming vehicles. This work presents a localization algorithm based on image detection and tracking by a single camera installed on a roadside unit (RSU). The vehicle position in the global reference frame is obtained thanks to a sequence of linear transformations utilizing intrinsic camera parameters, camera height, and pitch angle to obtain the vehicle\u2019s distance from the camera and, thus, its global latitude and longitude. The study brings an experimental analysis of both the localization accuracy, with an average error of 0.62 m, and detection reliability in terms of false positive (1.9%) and missed detection (3.6%) rates.<\/jats:p>","DOI":"10.3390\/s23167136","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T11:07:10Z","timestamp":1692011230000},"page":"7136","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Infrastructure-Based Vehicle Localization through Camera Calibration for I2V Communication Warning"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2281-6038","authenticated-orcid":false,"given":"Daniele","family":"Vignarca","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8403-355X","authenticated-orcid":false,"given":"Michele","family":"Vignati","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5316-7387","authenticated-orcid":false,"given":"Stefano","family":"Arrigoni","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4356-8814","authenticated-orcid":false,"given":"Edoardo","family":"Sabbioni","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1109\/TITS.2011.2119372","article-title":"A review of computer vision techniques for the analysis of urban traffic","volume":"12","author":"Buch","year":"2011","journal-title":"IEEE Trans. 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