Study on Human Hazardous Behavior Recognition and Monitoring System in Slide Facilities Based on Improved HRNet Network

In recent years, accidents involving slide playground equipment have frequently occurred due to various reasons, attracting significant attention. Reducing or even eliminating these accidental injuries has become an urgent technical issue to address. Currently, the safety management of slide playground facilities still relies on manual monitoring, and the level of technology for detecting and intelligently recognizing hazardous behaviors on slides needs improvement. This paper proposes a behavior detection system based on human skeleton sequence information to address the issue of recognizing hazardous behaviors on slides. To resolve the feature fusion loss problem that arises when HRNet extracts feature information from images of different resolutions, this paper introduces a Flow Alignment Module (FAM) and an Attention-aware Feature Fusion (AFF) module to improve the network structure. Experimental results show that the improved skeleton sequence extraction model exhibits good computational efficiency and accuracy on the dataset, achieving an accuracy rate of over 90%. The human behavior recognition system proposed in this paper effectively meets detection requirements, providing new technical assurance for the safe use of slide playground equipment.