Video Anomaly Detection: A Systematic Taxonomy and Analysis of Deep Models

Video anomaly detection VAD , a foundational pillar of modern computer vision, has garnered significant attention due to its wide-ranging real-world applications. Despite considerable progress, VAD persistently grapples with formidable challenges, particularly the scarcity of anomalies in real-world datasets and the complexities of accurate anomaly annotation. This survey investigates state-of-the-art VAD methodologies, synthesizing their core challenges and elucidating tailored solutions. Addressing the shortcomings of prior reviews, we propose a unified taxonomy that classifies methods according to input modalities: raw video, mid-level visual features, and high-level visual-semantic representations, providing a perspicuous framework to discern their unique attributes. To enrich comprehension, we present rigorous comparisons and analyses across established benchmark datasets. Anticipating future developments, we delineate promising research trajectories, such as semantic context learning enabled by contrastive language-image pre-training CLIP and multi-modal large language models MLLMs , to drive transformative advancements in the field. Furthermore, we meticulously examine the practical impediments existing approaches encounter in deployment in real-world environments.