Video anomaly detection (VAD) holds immense importance across diverse domains
such as surveillance, healthcare, and environmental monitoring. While numerous
surveys focus on conventional VAD methods, they often lack depth in exploring
specific approaches and emerging trends. This survey explores deep
learning-based VAD, expanding beyond traditional supervised training paradigms
to encompass emerging weakly supervised, self-supervised, and unsupervised
approaches. A prominent feature of this review is the investigation of core
challenges within the VAD paradigms including large-scale datasets, features
extraction, learning methods, loss functions, regularization, and anomaly score
prediction. Moreover, this review also investigates the vision language models
(VLMs) as potent feature extractors for VAD. VLMs integrate visual data with
textual descriptions or spoken language from videos, enabling a nuanced
understanding of scenes crucial for anomaly detection. By addressing these
challenges and proposing future research directions, this review aims to foster
the development of robust and efficient VAD systems leveraging the capabilities
of VLMs for enhanced anomaly detection in complex real-world scenarios. This
comprehensive analysis seeks to bridge existing knowledge gaps, provide
researchers with valuable insights, and contribute to shaping the future of VAD
research.

通过深度学习方法的视频异常检测（VAD）调查，本篇综述探索了传统监督训练范式之外的新兴弱监督、自监督和无监督方法，研究了 VAD 范式中的核心挑战，以及视觉语言模型（VLMs）作为强大的特征提取器在 VAD 中的作用，旨在增强复杂现实场景中异常检测的鲁棒性和效率，并填补现有知识差距，为研究人员提供有价值的见解，为 VAD 研究的未来贡献力量。