Pose-based Human Behavior Detection for Real-time Security Surveillance

Security is a critical function for protecting target individuals from potential threats. In particular, safeguarding officials such as politicians requires significant resources to ensure their safety. In this paper, we investigate a real-time video surveillance system for safeguarding and develop an accurate and efficient human behavior detection method. After defining four target behaviors associated with potential threats, we construct a video dataset for these behaviors. Using sequences of estimated human poses, we then implement a lightweight human behavior detection method. Specifically, our approach combines convolutional layers with a Transformer encoder to capture both local and global features of human behavior. Experimental results demonstrate that our network achieves an average accuracy of 96.84% with an inference time of 2.1 milliseconds. We expect that the proposed method will significantly reduce the operational cost of video surveillance while maintaining effective detection of potential security threats.