Cite this DOI

S. Samreen, S. S. (2023). AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance. *Journal of Science & Technology*, *8*(12), 156–167. https://doi.org/10.46243/jst.2023.v8.i12.pp156-167

@article{ssamreen2023aipowered,
  author    = {S. Samreen, S. Samreen},
  title     = {{AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance}},
  journal   = {Journal of Science \& Technology},
  year      = {2023},
  month     = {dec},
  volume    = {8},
  number    = {12},
  pages     = {156--167},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2023.v8.i12.pp156-167},
  url       = {https://doi.org/10.46243/jst.2023.v8.i12.pp156-167},
  language  = {en},
  abstract  = {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}
}

TY  - JOUR
TI  - AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance
AU  - S. Samreen, S. Samreen
JO  - Journal of Science & Technology
PY  - 2023
DA  - 2023/12/12/
VL  - 8
IS  - 12
SP  - 156
EP  - 167
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - 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
DO  - 10.46243/jst.2023.v8.i12.pp156-167
UR  - https://doi.org/10.46243/jst.2023.v8.i12.pp156-167
ER  -

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    "DOI": "10.46243/jst.2023.v8.i12.pp156-167",
    "URL": "https://doi.org/10.46243/jst.2023.v8.i12.pp156-167",
    "title": "AI-powered Approach for Accident Occurance Alerting from Traffic Surveillance",
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    "publisher": "Longman Publishers",
    "language": "en",
    "abstract": "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",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2023.v8.i12.pp156-167 gives all four in one JSON answer.