Human Behavior Recognition Based on Attention Mechanism and Bottleneck Residual Dual-Path Spatiotemporal Graph Convolutional Network

Video surveillance systems are crucial tools for enhancing security and technological prevention levels. Behavior recognition plays a vital role in violence prevention and the identification of hazardous activities within video surveillance systems. Addressing challenges such as the impact of redundant backgrounds and multi-angle shooting environments on human behavior recognition in surveillance videos, resulting in poor data processing capabilities and low accuracy, this paper proposes a novel behavior detection algorithm named BiST-GCN-CBAM (Bottleneck Residual Dual-Path Spatiotemporal Graph Convolutional Network with Attention Mechanism). The model integrates bottleneck residual connections and the Channel-wise Attention Mechanism (CBAM) into the framework of spatiotemporal graph convolution. The proposed model enhances the representation capability of human behavior features, facilitating efficient recognition. Additionally, the integrated attention mechanism allows the network to focus on relevant information while disregarding unnecessary details, effectively addressing the challenge of distinguishing key nodes for different behaviors and improving recognition accuracy. Experimental results on the Kinetic-skeleton and NTU-RGB+D datasets demonstrate that the algorithm effectively reduces network computational complexity while enhancing performance, leading to accurate human behavior recognition.