A Lightweight Dual-Branch System for Weakly-Supervised Video Anomaly Detection on Consumer Edge Devices

Abstract

The growing demand for intelligent security in consumer electronics, such as smart home cameras and personal monitoring systems, is often hindered by the high computational cost and large model sizes of advanced AI. These limitations prevent the effective deployment of real-time Video Anomaly Detection (VAD) on resource-constrained edge devices. To bridge this gap, this paper introduces Rule-based Video Anomaly Detection (RuleVAD), a novel, lightweight system engineered for high-efficiency and low-complexity threat detection directly on consumer hardware. RuleVAD features an innovative decoupled dual-branch architecture to minimize computational load. An implicit branch uses visual features for rapid, coarse-grained binary classification, efficiently filtering out normal activity to avoid unnecessary processing. For potentially anomalous or complex events, a multimodal explicit branch takes over. This branch leverages YOLO-World to detect objects and applies data mining to generate interpretable, text-based association rules from the scene. By aligning these rules with visual data, RuleVAD achieves a more nuanced, fine-grained classification, significantly reducing the false alarms common in vision-only systems. Extensive experiments on the XD-Violence and UCF-Crime benchmark datasets show that RuleVAD achieves superior performance, surpassing existing state-of-the-art methods in both accuracy and speed. Crucially, the entire system is optimized for low-power operation and is fully deployable on an NVIDIA Jetson Nano board, demonstrating its practical feasibility for bringing advanced, real-time security monitoring to everyday consumer electronic devices.