Spatio-Temporal Graph Convolutional Networks for Accident Detection in Video Surveillance

An innovative accident-detecting system based on spatio-temporal graph convolutional networks to enhance the observation of real-time traffic events is proposed for this research. It incorporates the automatic detection of accidents and peculiar events by processing real-time video footage from cameras and sensor data to enhance road safety and expedite alerts to concerned emergency services. The performance of the ST-GCN is evaluated compared to other models currently in use which comprise Optical Flow Networks, Region-based Convolutional Neural Networks (R-CNN), and 3D Convolutional Neural Networks (3D-CNN). The results show that ST-GCN is a better option for accident detection in challenging scenarios in traffic scenes because it captures spatial positions along with time relationships, and this offers higher performance than competing approaches. The results highlight the potential of this method in improving incident detection and reaction systems, thereby leading to safer roads.