A Vision-Based Traffic Accident Analysis and Tracking system from Traffic Surveillance Video

Traffic accidents pose significant challenges to road safety and transportation management worldwide. Timely and accurate analysis of these incidents is crucial for effective response and mitigation. This paper presents a novel VisionBased Traffic Accident Analysis and Tracking System designed to detect, analyze, and track traffic accidents from surveillance videos using advanced image processing techniques. Recent research methodology has unlimited in realizing the object trajectories of vehicle accident connected information detecting and take out the features relationships. The proposed research paper recommends a road traffic accident tracking systems that assist to establish whether every video sequence demonstrates mishap by producing and considering vehicle object trajectories using Vehicle Influence Location Generator and CNN classifier. In the proposed system, the vehicle influence location generator includes three parameters: vehicle object trajectories the distances between vehicles and accident analysis. First parameter, vehicle portions are identified by rectangle bounding box in green colour. The second parameter, vehicle portion centre positions are represented by dots using Region of Interest (ROI). The final parameters, the distances between previous vehicle frame and current vehicle frame are represented by points. The proposed system considered threshold value for distance calculation and update the values. Finally, the opportunity of a disaster is characterized by red circles. A CNN will be used to analyze the accident from the Vehicle Influence Location Generator produced by vehicle object trajectories. The proposed model was trained using CADP dataset in the training and testing stage, and it was able to obtain an estimated 96.5 % accuracy rate in overall proposed traffic accident detection.