{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-discovery-and-geotagging-of-objects","title":"Automatic Discovery and Geotagging of Objects from Street View Imagery","arxiv_id":"1708.08417","date":"2017-08-28","proceeding":null,"authors":["Vladimir A. Krylov","Eamonn Kenny","Rozenn Dahyot"],"abstract":"Many applications such as autonomous navigation, urban planning and asset\nmonitoring, rely on the availability of accurate information about objects and\ntheir geolocations. In this paper we propose to automatically detect and\ncompute the GPS coordinates of recurring stationary objects of interest using\nstreet view imagery. Our processing pipeline relies on two fully convolutional\nneural networks: the first segments objects in the images while the second\nestimates their distance from the camera. To geolocate all the detected objects\ncoherently we propose a novel custom Markov Random Field model to perform\nobjects triangulation. The novelty of the resulting pipeline is the combined\nuse of monocular depth estimation and triangulation to enable automatic mapping\nof complex scenes with multiple visually similar objects of interest. We\nvalidate experimentally the effectiveness of our approach on two object\nclasses: traffic lights and telegraph poles. The experiments report high object\nrecall rates and GPS accuracy within 2 meters, which is comparable with the\nprecision of single-frequency GPS receivers.","url_abs":"http://arxiv.org/abs/1708.08417v2","url_pdf":"http://arxiv.org/pdf/1708.08417v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-discovery-and-geotagging-of-objects","repo_url":"https://github.com/sasha-kap/CV-to-Maps","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"monocular-depth-estimation","task_name":"Monocular Depth Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}