  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.
