Review of Video Anomaly Detection from Closed-Set to Open-Set

Video anomaly detection (VAD) has evolved from handcrafted feature-based methods to deep learning and foundation model-based approaches, enabling improved detection of complex and previously unseen events. This review examines this evolution through four paradigms: closed-set, open-set, open-vocabulary, and open-world VAD. A unified taxonomy is developed to organize representative methods according to learning paradigms, model architectures, supervision strategies, and semantic capabilities. The review further compares benchmark datasets, evaluation protocols, and performance metrics, with emphasis on the challenges of detecting and recognizing unseen anomalies in realistic environments. Recent advances in vision–language models (VLMs) are discussed in terms of their ability to incorporate language knowledge and open-ended semantic reasoning into VAD. Key challenges related to generalization, computational efficiency, multimodal understanding, and real-world deployment are identified. Finally, emerging directions involving large language models (LLMs), multimodal large language models (MLLMs), and video large language models (Video-LLMs), together with agentic AI and autonomous reasoning, are discussed toward more adaptive and interpretable VAD systems.