Graph-Based Temporal Attention Network for Anomaly Recognition in Internet of Things Video Surveillance

Abstract

Accurate anomaly recognition in video surveillance is critical to ensuring public safety, infrastructure protection, and intelligent security operations. However, existing methods often fail to generalize across diverse and dynamic environments due to their limited capacity to model complex spatio-temporal patterns. In this article, we propose a novel graph-based temporal attention-memory network (G-TAMNet) that significantly advances video anomaly detection by capturing intricate temporal dependencies as well as spatial relationships. By integrating temporal self-attention into a graph convolutional network (GCN) framework, our model enhances the learning of salient and contextually relevant features that traditional approaches tend to overlook. Furthermore, to enable deployment in resource-constrained settings, such as Internet of Things (IoT)-based surveillance systems, we incorporate model quantization strategies that reduce computational overhead without compromising detection accuracy. Extensive evaluations of three widely used benchmarks - UCF-Crime, LAD-2000, and RWF-2000 - demonstrate the effectiveness of the proposed G-TAMNet model, achieving accuracy, precision, recall, and F1 -scores of 54.3%/61.1%/59.01%/61.1%, 79.3%/69.1%/70.0%/69.1%, and 94.0%/94.0%/94.2%/94.1%, respectively. These results reflect consistent performance gains of 3.3%, 9.9%, and 0.74% in accuracy over existing state-of-the-art methods on the corresponding datasets. Such improvements underscore the robustness, scalability, and practical viability of G-TAMNet for real-time anomaly detection in resource-constrained IoT surveillance systems. In addition, its reliable generalization across diverse environments highlights the potential of the model to advance intelligent security analytics and transform real-world applications in the domain of information forensics.

Keywords: Anomalies recognition, graph convolution network, surveillance video, temporal attention memory