{"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/detecting-work-zones-in-shrp-2-nds-videos","title":"Detecting Work Zones in SHRP 2 NDS Videos Using Deep Learning Based Computer Vision","arxiv_id":"1811.04250","date":"2018-11-10","proceeding":null,"authors":["Franklin Abodo","Robert Rittmuller","Brian Sumner","Andrew Berthaume"],"abstract":"Naturalistic driving studies seek to perform the observations of human driver\nbehavior in the variety of environmental conditions necessary to analyze,\nunderstand and predict that behavior using statistical and physical models. The\nsecond Strategic Highway Research Program (SHRP 2) funds a number of\ntransportation safety-related projects including its primary effort, the\nNaturalistic Driving Study (NDS), and an effort supplementary to the NDS, the\nRoadway Information Database (RID). This work seeks to expand the range of\nanswerable research questions that researchers might pose to the NDS and RID\ndatabases. Specifically, we present the SHRP 2 NDS Video Analytics (SNVA)\nsoftware application, which extracts information from NDS-instrumented\nvehicles' forward-facing camera footage and efficiently integrates that\ninformation into the RID, tying the video content to geolocations and other\ntrip attributes. Of particular interest to researchers and other stakeholders\nis the integration of work zone, traffic signal state and weather information.\nThe version of SNVA introduced in this paper focuses on work zone detection,\nthe highest priority. The ability to automate the discovery and cataloging of\nthis information, and to do so quickly, is especially important given the two\npetabyte (2PB) size of the NDS video data set.","url_abs":"http://arxiv.org/abs/1811.04250v1","url_pdf":"http://arxiv.org/pdf/1811.04250v1.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":"detecting-work-zones-in-shrp-2-nds-videos","repo_url":"https://github.com/VolpeUSDOT/SNVA","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"detecting-work-zones-in-shrp-2-nds-videos","repo_url":"https://github.com/securade/hub","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}