{"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/traffic-surveillance-camera-calibration-by-3d","title":"Traffic Surveillance Camera Calibration by 3D Model Bounding Box Alignment for Accurate Vehicle Speed Measurement","arxiv_id":"1702.06451","date":"2017-02-21","proceeding":null,"authors":["Jakub Sochor","Roman Juránek","Adam Herout"],"abstract":"In this paper, we focus on fully automatic traffic surveillance camera\ncalibration, which we use for speed measurement of passing vehicles. We improve\nover a recent state-of-the-art camera calibration method for traffic\nsurveillance based on two detected vanishing points. More importantly, we\npropose a novel automatic scene scale inference method. The method is based on\nmatching bounding boxes of rendered 3D models of vehicles with detected\nbounding boxes in the image. The proposed method can be used from arbitrary\nviewpoints, since it has no constraints on camera placement. We evaluate our\nmethod on the recent comprehensive dataset for speed measurement BrnoCompSpeed.\nExperiments show that our automatic camera calibration method by detection of\ntwo vanishing points reduces error by 50% (mean distance ratio error reduced\nfrom 0.18 to 0.09) compared to the previous state-of-the-art method. We also\nshow that our scene scale inference method is more precise, outperforming both\nstate-of-the-art automatic calibration method for speed measurement (error\nreduction by 86% -- 7.98km/h to 1.10km/h) and manual calibration (error\nreduction by 19% -- 1.35km/h to 1.10km/h). We also present qualitative results\nof the proposed automatic camera calibration method on video sequences obtained\nfrom real surveillance cameras in various places, and under different lighting\nconditions (night, dawn, day).","url_abs":"http://arxiv.org/abs/1702.06451v2","url_pdf":"http://arxiv.org/pdf/1702.06451v2.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":[],"tasks":[{"task_slug":"camera-calibration","task_name":"Camera Calibration"},{"task_slug":"vehicle-speed-estimation","task_name":"Vehicle Speed Estimation"}],"methods":[{"method_slug":"speed","method_name":"SPEED"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/vehicle-speed-estimation-on-brnocompspeed","task":"Vehicle Speed Estimation","dataset":"BrnoCompSpeed","model":"Edgelets + BBScale + reg","rank_in_archive_order":4,"of":4,"metrics":{"99-th Percentile Speed Measurement Error (km/h)":"3.05","Mean Speed Measurement Error (km/h)":"1.10","Median Speed Measurement Error (km/h)":"0.97"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1702.06451","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}