Accident Detection System Using Intelligent Surveillance

Accident detection in urban environments is crucial for enhancing public safety and reducing emergency response times.This research proposes an innovative system leveraging intelligent surveillance techniques to detect and respond to accidents promptly.The proposed system incorporates advanced computer vision algorithms and to analyze real-time video feeds from surveillance cameras.The framework begins with the acquisition of video data from strategically placed cameras in urban areas.Yolo version model is employed to detect accidents in the video frames.Subsequently, the system employs motion analysis and anomaly detection algorithms to identify unusual patterns or events that may indicate a potential accident.To enhance accuracy and reduce false positives, the system incorporates contextual information, such as road conditions, weather, and traffic flow.Machine learning algorithms are trained on historical data to better understand normal patterns and differentiate them from anomalous situations.Additionally, the system adapts in real-time to changes in the environment, ensuring robust performance under varying conditions.Upon the detection of a potential accident, the system triggers immediate alerts to emergency services, providing them with the precise location and nature of the incident.The integration of geospatial information enables emergency responders to reach the scene rapidly, improving the overall response time.The proposed intelligent surveillance system holds the potential to significantly improve safety by automating accident detection and response.The fusion of computer vision and real-time data analysis establishes a proactive approach to accident prevention, ultimately contributing to the creation of safer and more resilient smart cities. Future work will focus on scalability, realworld deployment, and continuous improvement through feedback loops from emergency response systems.