Abstract: Accident detection using computer vision and video surveillance has developed into a useful but challenging task. This research suggests the identification of traffic accidents. The suggested framework makes use of an effective centroid -based GMM algorithm for surveillance footage after accurately detecting objects using the axis bounding box technique. The suggested architecture offers a reliable way to get common road traffic CCTV surveillance footage to have a high Detection Rate and a low False Alarm Rate. Using the suggested dataset, this framework was tested under a variety of situations, including bright sunlight, poor visibility, rain, hail, and snow. This framework was shown to be efficient and opens the door for the creation of general-purpose real-time vehicle accident detection systems. Additionally, this project makes use of the Geopy module to record the real-time.
Keywords: Road Accidents, Intelligent Transportation System, Real Time Monitoring, Emergency Response, Gaussian Mixture Model (GMM) Algorithm.
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DOI:
10.17148/IJARCCE.2023.124171
