Video Anomaly Detection in 10 Years: A Survey and Outlook

TL;DR

This survey reviews a decade of video anomaly detection research, emphasizing deep learning, emerging paradigms like weakly supervised methods, and the potential of vision language models to enhance detection accuracy.

Contribution

It provides a comprehensive overview of traditional and emerging VAD approaches, highlighting challenges and proposing future directions including the integration of vision language models.

Findings

Deep learning-based VAD methods dominate current research.

Vision language models offer promising features for improved anomaly detection.

Identified key challenges such as dataset scale and feature extraction.

Abstract

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.