IDG-ViolenceNet: A Video Violence Detection Model Integrating Identity-Aware Graphs and 3D-CNN
Hong Huang, Qingping Jiang
Abstract
Video violence detection plays a crucial role in intelligent surveillance and public safety, yet existing methods still face challenges in modeling complex multi-person interactions. To address this, we propose IDG-ViolenceNet, a dual-stream video violence detection model that integrates identity-aware spatiotemporal graphs with three-dimensional convolutional neural networks (3D-CNN). Specifically, the model utilizes YOLOv11 for high-precision person detection and cross-frame identity tracking, constructing a dynamic spatiotemporal graph that encodes spatial proximity, temporal continuity, and individual identity information. On this basis, a GINEConv branch extracts structured interaction features, while an R3D-18 branch models local spatiotemporal patterns. The two representations are fused in a dedicated module for cross-modal feature integration. Experimental results show that IDG-ViolenceNet achieves accuracies of 97.5%, 99.5%, and 89.4% on the Hockey Fight, Movies Fight, and RWF-2000 datasets, respectively, significantly outperforming state-of-the-art methods. Additionally, ablation studies validate the contributions of key components in improving detection accuracy and robustness.