Real-Time Recognition of Dangerous Human Actions Using Lightweight Pose Estimation and Spatio-Temporal Graph Networks

Identification of dangerous human actions is of vital importance for safety monitoring. In response to the limitations of traditional methods, this study has developed an efficient pedestrian dangerous behavior recognition system, aiming to enhance monitoring capabilities and having practical application value. The study adopts a modular design, consisting of four core modules: human detection, posture estimation, behavior recognition, and result output. It can detect and warn of five dangerous behaviors such as throwing objects and climbing obstacles in real time. Among them, the posture estimation module addresses the shortcomings of the classic HRNet by adopting the improved SCite-HRNet model. This model optimizes computational efficiency and feature expression ability, ensuring accuracy (COCOval2017 dataset AP value of 65.9) while significantly reducing computational load (parameter quantity is only 18% of MobileNetV2), significantly improving its applicability on mobile devices. Experimental verification has demonstrated the effectiveness of the model improvement. The system is developed using PyTorch on the Ubuntu system, utilizing CUDA acceleration and multi-threading processing. It achieves a real-time processing speed of at least 25 frames per second on the RTX 2080Ti graphics card. Tests based on 15,000 multi-scenario labeled images show that the system has good robustness in complex environments. The system provides an intuitive visual monitoring interface, and the final classification accuracy reaches 99%.