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Detection of Accidents under Low Light Monitoring Conditions

  • Unique Paper ID: 159761
  • Volume: 9
  • Issue: 12
  • PageNo: 368-373
  • Abstract:
  • In recent times traffic pollution has become a major problem owing to increased urban population and greater number of motor vehicles, and traffic jams can be caused by accidents that result in injury or loss to those involved while also causing wasted time for other drivers who are unable to move. You may recognise and track numerous things, such as automobiles, pedestrians, and other pertinent features, by applying Faster R-CNN to CCTV footage. The combination of faster R-CNN and ODTS may detect irregular occurrences such as accidents or strange behaviour in CCTV images. The algorithm examines the video frames and detects bounding boxes around the items of interest. Typical object identification frameworks focus on recognising things within individual frames without taking into account their temporal interactions. However, it is feasible to follow the movement of objects and give them unique IDs by comparing the bounding boxes of objects across subsequent frames. The system can monitor and assign a unique ID to each moving object consistently by keeping a history of object IDs. This tracking feature allows applications such as video surveillance, autonomous cars, and activity identification, where the movement and behaviour of objects over time are critical for analysis and decision-making.
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  • ISSN: 2349-6002
  • Volume: 9
  • Issue: 12
  • PageNo: 368-373

Detection of Accidents under Low Light Monitoring Conditions

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