Review of Video Anomaly Detection from Closed-Set to Open-Set
Year: 2026
Type: article
Abstract: 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.
Source: Journal of Imaging
Authors: Duc Tri Phan, Hong Duc Nguyen
Institutions: Duy Tan University, Nanyang Technological University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,172
OpenAlex ID: W7225253128