{ "ok": true, "doi": "10.46243/jst.2023.v8.i12.pp156-167", "doi_display": "10.46243/jst.2023.v8.i12.pp156-167", "doi_url": "https://doi.org/10.46243/jst.2023.v8.i12.pp156-167", "state": "registered", "url": "https://www.jst.org.in/index.php/pub/article/view/882", "title": "AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance", "version": 2, "registered_via": "crossref", "prefix": { "prefix": "10.46243", "status": "live" }, "registrant": { "name": "Longman Publishers", "kind": "publisher", "country": "India" }, "reserved_at": null, "registered_at": "2026-09-29 22:00:28", "updated_at": "2026-09-29 23:59:47", "withdrawn_at": null, "withdrawn_reason": null, "record": { "format": "smartscholars-doi-metadata/1.0", "doi": "10.46243/jst.2023.v8.i12.pp156-167", "referent": "Creation", "type": "JournalArticle", "structural_type": "Digital", "modes": [ "Visual" ], "characters": [ "Language" ], "titles": [ { "value": "AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance", "type": "PrincipalTitle", "lang": "en" } ], "identifiers": [ { "type": "DOI", "value": "10.46243/jst.2023.v8.i12.pp156-167" } ], "agents": [ { "role": "author", "name": { "given": "S. Samreen", "family": "S. Samreen" }, "sequence": "first" }, { "role": "publisher", "name": { "org": "Longman Publishers" } } ], "dates": { "published": "2023-12-12", "date_type": "PublicationDate", "online": "2023-12-12" }, "language": "en", "container": { "type": "Journal", "titles": [ { "value": "Journal of Science & Technology", "type": "PrincipalTitle" } ], "identifiers": [ { "type": "ISSN", "value": "2456-5660", "medium": "electronic" } ], "volume": "8", "issue": "12", "pages": { "first": "156", "last": "167" } }, "links": [ { "url": "https://www.jst.org.in/index.php/pub/article/view/882", "return_type": "text/html", "primary": true }, { "url": "https://www.jst.org.in/index.php/pub/article/download/882/809", "purpose": "text-mining", "return_type": "application/pdf" }, { "url": "https://www.jst.org.in/index.php/pub/article/download/882/1679", "purpose": "text-mining", "return_type": "application/xml" }, { "url": "https://jst.org.in/admin/uploads/A22.%20Accident%20Detection.pdf", "purpose": "similarity-checking" } ], "abstract": { "value": "v Accidents have been a major cause of deaths in India. More than 80% of accident-related deaths occur not due to the accident itself but the lack of timely help reaching the accident victims. In highways where the traffic is light and fast-paced an accident victim could be left unattended for a long time. The intent is to create a system which would detect an accident based on the live feed of video from a CCTV camera installed on a highway. The idea is to take each frame of a video and run it through a deep learning convolution neural network model which has been trained to classify frames of a video into accident or non-accident. Convolutional Neural Networks has proven to be a fast and accurate approach to classify images. CNN based image classifiers have given accuracy’s of more than 95% for comparatively smaller datasets and require less preprocessing as compared to other image classifying algorithms", "lang": "en" }, "license": { "url": "https://creativecommons.org/licenses/by/4.0/", "start": "2023-12-12", "applies_to": "vor" }, "references": [ { "key": "ref1", "unstructured": "“Global status report on road safety 2015”, World Health Organization, 2019. [Online]. Available: http://www.who.int/violence_injury_ prevention/road_safety_status/20 15/en/. [Accessed: 07-Mar- 2019]" }, { "key": "ref2", "doi": "10.1109/indcon.2012.6420642", "unstructured": "Prabakar, S., et al. “An enhanced accident detection and victim status indicating system: Prototype.” India Conference (INDICON), 2012 Annual IEEE. IEEE, 2012" }, { "key": "ref3", "unstructured": "“Lexus Enform”, Lexus, 2019. [Online]. Available: https://www.lexus.com/enform. [Accessed: 07-Mar- 2019]" }, { "key": "ref4", "doi": "10.1001/jama.2020.17588", "unstructured": "“OnStar Safety and Security Services”, Onstar. com, 2019. [Online]. Available: https://www. onstar.com/us/en/services/safety-security/. [Accessed: 07-Mar- 2019]" }, { "key": "ref5", "unstructured": "“SOSmart automatic car crash detection and notification app”, SOSmart automatic car crash detection app, 2019. [Online]. Available: http:// www.sosmartapp.com. [Accessed: 07-Mar- 2019]" }, { "key": "ref6", "unstructured": "C. Kockan, “Communication between vehicles” PhD thesis, Istanbul Technical University, 2008" }, { "key": "ref7", "doi": "10.1007/s11276-015-0976-2", "unstructured": "Zeng, Yuanyuan, Deshi Li, and Athanasios V. Vasilakos. “Opportunistic fleets for road event detection in vehicular sensor networks.” Wireless Networks 22.2 (2016): 503-521" }, { "key": "ref8", "doi": "10.1109/cvpr.2016.308", "unstructured": "Szegedy, Christian, et al. “Rethinking the inception architecture for computer vision.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016" }, { "key": "ref9", "doi": "10.1109/cvpr.2015.7298594", "unstructured": "Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015" } ], "record": { "registrant": "Longman Publishers", "registered": "2024-02-16", "updated": "2026-09-17", "issue_number": 1, "source": "crossref-api", "source_agency": "Crossref (member 25296)" } }, "record_sha256": "e2584ffb4053f6702544db11af4ce87c5bde9a6f016aa4718dd824631d45c482", "handle": { "synced_at": "2026-10-01 18:12:46", "url": "https://www.jst.org.in/index.php/pub/article/view/882" }, "links": { "record_page": "https://registry.smartscholars.in/record.php?doi=10.46243%2Fjst.2023.v8.i12.pp156-167", "system_metadata": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2023.v8.i12.pp156-167&as=system", "history": "https://registry.smartscholars.in/api.php?action=history&doi=10.46243%2Fjst.2023.v8.i12.pp156-167", "kernel_xml": "https://registry.smartscholars.in/resolve.php?doi=10.46243%2Fjst.2023.v8.i12.pp156-167&as=xml" } }