{
    "ok": true,
    "doi": "10.46243/jst.2023.v8.i12.pp156-167",
    "events": [
        {
            "seq": 1,
            "kind": "register",
            "at": "2026-09-29 22:00:28",
            "by": "Administrator (admin)",
            "by_kind": "user",
            "note": "registered at Crossref; record read from api.crossref.org",
            "changes": [
                {
                    "path": "abstract.lang",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "abstract.value",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "agents.0.name.family",
                    "from": null,
                    "to": "S. Samreen"
                },
                {
                    "path": "agents.0.name.given",
                    "from": null,
                    "to": "S. Samreen"
                },
                {
                    "path": "agents.0.role",
                    "from": null,
                    "to": "author"
                },
                {
                    "path": "agents.0.sequence",
                    "from": null,
                    "to": "first"
                },
                {
                    "path": "agents.1.name.org",
                    "from": null,
                    "to": "Longman Publishers"
                },
                {
                    "path": "agents.1.role",
                    "from": null,
                    "to": "publisher"
                },
                {
                    "path": "characters.0",
                    "from": null,
                    "to": "Language"
                },
                {
                    "path": "container.identifiers.0.medium",
                    "from": null,
                    "to": "electronic"
                },
                {
                    "path": "container.identifiers.0.type",
                    "from": null,
                    "to": "ISSN"
                },
                {
                    "path": "container.identifiers.0.value",
                    "from": null,
                    "to": "2456-5660"
                },
                {
                    "path": "container.issue",
                    "from": null,
                    "to": "12"
                },
                {
                    "path": "container.pages.first",
                    "from": null,
                    "to": "156"
                },
                {
                    "path": "container.pages.last",
                    "from": null,
                    "to": "167"
                },
                {
                    "path": "container.titles.0.type",
                    "from": null,
                    "to": "PrincipalTitle"
                },
                {
                    "path": "container.titles.0.value",
                    "from": null,
                    "to": "Journal of Science & Technology"
                },
                {
                    "path": "container.type",
                    "from": null,
                    "to": "Journal"
                },
                {
                    "path": "container.volume",
                    "from": null,
                    "to": "8"
                },
                {
                    "path": "dates.date_type",
                    "from": null,
                    "to": "PublicationDate"
                },
                {
                    "path": "dates.online",
                    "from": null,
                    "to": "2023-12-12"
                },
                {
                    "path": "dates.published",
                    "from": null,
                    "to": "2023-12-12"
                },
                {
                    "path": "doi",
                    "from": null,
                    "to": "10.46243/jst.2023.v8.i12.pp156-167"
                },
                {
                    "path": "format",
                    "from": null,
                    "to": "smartscholars-doi-metadata/1.0"
                },
                {
                    "path": "identifiers.0.type",
                    "from": null,
                    "to": "DOI"
                },
                {
                    "path": "identifiers.0.value",
                    "from": null,
                    "to": "10.46243/jst.2023.v8.i12.pp156-167"
                },
                {
                    "path": "language",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "license.applies_to",
                    "from": null,
                    "to": "vor"
                },
                {
                    "path": "license.start",
                    "from": null,
                    "to": "2023-12-12"
                },
                {
                    "path": "license.url",
                    "from": null,
                    "to": "https://creativecommons.org/licenses/by/4.0/"
                },
                {
                    "path": "links.0.primary",
                    "from": null,
                    "to": true
                },
                {
                    "path": "links.0.return_type",
                    "from": null,
                    "to": "text/html"
                },
                {
                    "path": "links.0.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/view/882"
                },
                {
                    "path": "links.1.purpose",
                    "from": null,
                    "to": "text-mining"
                },
                {
                    "path": "links.1.return_type",
                    "from": null,
                    "to": "application/pdf"
                },
                {
                    "path": "links.1.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/download/882/809"
                },
                {
                    "path": "links.2.purpose",
                    "from": null,
                    "to": "text-mining"
                },
                {
                    "path": "links.2.return_type",
                    "from": null,
                    "to": "application/xml"
                },
                {
                    "path": "links.2.url",
                    "from": null,
                    "to": "https://www.jst.org.in/index.php/pub/article/download/882/1679"
                },
                {
                    "path": "links.3.purpose",
                    "from": null,
                    "to": "similarity-checking"
                },
                {
                    "path": "links.3.url",
                    "from": null,
                    "to": "https://jst.org.in/admin/uploads/A22.%20Accident%20Detection.pdf"
                },
                {
                    "path": "modes.0",
                    "from": null,
                    "to": "Visual"
                },
                {
                    "path": "record.issue_number",
                    "from": null,
                    "to": 1
                },
                {
                    "path": "record.registered",
                    "from": null,
                    "to": "2024-02-16"
                },
                {
                    "path": "record.registrant",
                    "from": null,
                    "to": "Longman Publishers"
                },
                {
                    "path": "record.source",
                    "from": null,
                    "to": "crossref-api"
                },
                {
                    "path": "record.source_agency",
                    "from": null,
                    "to": "Crossref (member 25296)"
                },
                {
                    "path": "record.updated",
                    "from": null,
                    "to": "2026-09-17"
                },
                {
                    "path": "references.0.key",
                    "from": null,
                    "to": "ref1"
                },
                {
                    "path": "references.0.unstructured",
                    "from": null,
                    "to": "“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]"
                },
                {
                    "path": "references.1.doi",
                    "from": null,
                    "to": "10.1109/indcon.2012.6420642"
                },
                {
                    "path": "references.1.key",
                    "from": null,
                    "to": "ref2"
                },
                {
                    "path": "references.1.unstructured",
                    "from": null,
                    "to": "Prabakar, S., et al. “An enhanced accident detection and victim status indicating system: Prototype.” India Conference (INDICON), 2012 Annual IEEE. IEEE, 2012"
                },
                {
                    "path": "references.2.key",
                    "from": null,
                    "to": "ref3"
                },
                {
                    "path": "references.2.unstructured",
                    "from": null,
                    "to": "“Lexus Enform”, Lexus, 2019. [Online]. Available: https://www.lexus.com/enform. [Accessed: 07-Mar- 2019]"
                },
                {
                    "path": "references.3.doi",
                    "from": null,
                    "to": "10.1001/jama.2020.17588"
                },
                {
                    "path": "references.3.key",
                    "from": null,
                    "to": "ref4"
                },
                {
                    "path": "references.3.unstructured",
                    "from": null,
                    "to": "“OnStar Safety and Security Services”, Onstar. com, 2019. [Online]. Available: https://www. onstar.com/us/en/services/safety-security/. [Accessed: 07-Mar- 2019]"
                },
                {
                    "path": "references.4.key",
                    "from": null,
                    "to": "ref5"
                },
                {
                    "path": "references.4.unstructured",
                    "from": null,
                    "to": "“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]"
                },
                {
                    "path": "references.5.key",
                    "from": null,
                    "to": "ref6"
                },
                {
                    "path": "references.5.unstructured",
                    "from": null,
                    "to": "C. Kockan, “Communication between vehicles” PhD thesis, Istanbul Technical University, 2008"
                },
                {
                    "path": "references.6.doi",
                    "from": null,
                    "to": "10.1007/s11276-015-0976-2"
                },
                {
                    "path": "references.6.key",
                    "from": null,
                    "to": "ref7"
                },
                {
                    "path": "references.6.unstructured",
                    "from": null,
                    "to": "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"
                },
                {
                    "path": "references.7.doi",
                    "from": null,
                    "to": "10.1109/cvpr.2016.308"
                },
                {
                    "path": "references.7.key",
                    "from": null,
                    "to": "ref8"
                },
                {
                    "path": "references.7.unstructured",
                    "from": null,
                    "to": "Szegedy, Christian, et al. “Rethinking the inception architecture for computer vision.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2016"
                },
                {
                    "path": "references.8.doi",
                    "from": null,
                    "to": "10.1109/cvpr.2015.7298594"
                },
                {
                    "path": "references.8.key",
                    "from": null,
                    "to": "ref9"
                },
                {
                    "path": "references.8.unstructured",
                    "from": null,
                    "to": "Szegedy, Christian, et al. “Going deeper with convolutions.” Proceedings of the IEEE conference on computer vision and pattern recognition. 2015"
                },
                {
                    "path": "referent",
                    "from": null,
                    "to": "Creation"
                },
                {
                    "path": "structural_type",
                    "from": null,
                    "to": "Digital"
                },
                {
                    "path": "titles.0.lang",
                    "from": null,
                    "to": "en"
                },
                {
                    "path": "titles.0.type",
                    "from": null,
                    "to": "PrincipalTitle"
                },
                {
                    "path": "titles.0.value",
                    "from": null,
                    "to": "AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance"
                },
                {
                    "path": "type",
                    "from": null,
                    "to": "JournalArticle"
                }
            ],
            "record_after": {
                "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)"
                }
            },
            "url_after": "https://www.jst.org.in/index.php/pub/article/view/882",
            "sha256_before": null,
            "sha256_after": "1718c149a04c9dbed41293b1b71ce51699282bf0f20e0baf9ac02a271e851808"
        },
        {
            "seq": 2,
            "kind": "update",
            "at": "2026-09-29 23:59:47",
            "by": "Administrator (admin)",
            "by_kind": "user",
            "note": "record re-read from api.crossref.org",
            "changes": [
                {
                    "path": "container.titles.0.value",
                    "from": "Journal of Science & Technology",
                    "to": "Journal of Science & Technology"
                }
            ],
            "record_after": {
                "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)"
                }
            },
            "url_after": "https://www.jst.org.in/index.php/pub/article/view/882",
            "sha256_before": "1718c149a04c9dbed41293b1b71ce51699282bf0f20e0baf9ac02a271e851808",
            "sha256_after": "e2584ffb4053f6702544db11af4ce87c5bde9a6f016aa4718dd824631d45c482"
        }
    ]
}