MG-KG: Unsupervised video anomaly detection based on motion guidance and knowledge graph

Abstract

Unsupervised Video Anomaly Detection (VAD) is a challenging and research-valuable task that is trained with only normal samples to detect anomalous samples. However, current solutions face two key issues: (1) a lack of spatio-temporal linkage in video data, and (2) limited interpretability of VAD results. To address these, we propose a new method named Motion Guidance-Knowledge Graph (MG-KG), inspired by video saliency detection and video understanding methods. Specifically, MG-KG has two components: the Motion Guidance Network (MGNet) and the Knowledge Graph retrieval for VAD (VAD-KG). MGNet emphasizes motion in the video foreground, crucial for real-time surveillance, while VAD-KG builds a knowledge graph to store structured video information and retrieve it during testing, enhancing interpretability. This combination improves both generalization and understanding in VAD for smart surveillance systems. Additionally, since training data has only normal samples, we propose a training baseline strategy, a tabu search strategy, and a score rectification strategy to enhance MG-KG for video anomaly detection tasks, which can further exploit the potential of MG-KG and significantly improve the performance of VAD. Extensive experiments demonstrate that MG-KG achieves competitive results in VAD for intelligent video surveillance.