Research on Logical Understanding of Video Surveillance Systems Based on Knowledge Graphs

Video surveillance systems are widely used in various fields, and enhancing the intelligence of video surveillance for better video understanding is a crucial topic. This paper conducts research on video logic understanding in surveillance systems using knowledge graphs and designs a video understanding system framework based on both prior and posterior knowledge graphs. The application system is developed using a Browser Server architecture. Finally, an experimental environment is set up in a 36 channel indoor duty scenario, resulting in a video understanding system with decision support capabilities.