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
[1] Abinesh Kannan S, Akash M, Brajesh Choudhary B, Maheswari M, Amsavalli K, "Road Accident Detection and Notification for Speed Recovery," International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2023.124171
