Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection
Sharif, S., Zorto, A., Brown, V. K. and Elmedany, W. 2023. Scalable Machine Learning Model for Highway CCTV Feed Real-Time Car Accident and Damage Detection. 3ICT 2023: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies. University of Bahrain, Bahrain 20 - 21 Nov 2023 IEEE.
|Authors||Sharif, S., Zorto, A., Brown, V. K. and Elmedany, W.|
This study investigates the potential advantages of employing computer vision algorithms to enhance real-time accident detection and response on highways using CCTV feed. Traditional techniques rely on retrospective data, which can decrease response times and precision. Computer vision algorithms have the potential to enhance detection speed and precision, resulting in quicker emergency response and monitoring of traffic flow. The primary objective of this study is to identify the advantages of utilising computer vision algorithms and the data gathered through them to enhance road safety measures and reduce the occurrence of accidents. This study is anticipated to result in quicker emergency response times, the identification of areas where statistically more accidents are likely to occur, and the use of collected data for research purposes, which can lead to enhanced road safety measures. Using computer vision algorithms for accident detection and response has the potential to reduce the human and monetary costs associated with traffic accidents.
|Conference||3ICT 2023: International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies|
|Accepted author manuscript|
File Access Level
|Publication process dates|
|Accepted||14 Sep 2023|
|Deposited||25 Sep 2023|
|Copyright holder||© 2023, IEEE|
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