Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. Such reviews would serve as quick references to grasp current challenges, research trends, and future directions. In this paper, we present such a review, examining models and datasets from various perspectives. We emphasize the critical relationship between model and dataset, where the quality and diversity of datasets profoundly influence model performance, and dataset development adapts to the evolving needs of emerging approaches. Our review identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, we introduce a new dataset, Multi-Scenario Anomaly Detection (MSAD), comprising 14 distinct scenarios captured from various camera views. Our dataset has diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. We conduct an in-depth analysis of recent representative models using MSAD and highlight its potential in addressing the challenges of detecting anomalies across diverse and evolving surveillance scenarios. [Project website: https://msad-dataset.github.io/]
Advancing Adaptive Multi-Stage Video Anomaly Reasoning: A Benchmark Dataset and Method
School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学计算机科学与技术学院（深圳校区））；Shenzhen Key Laboratory of Visual Object Detection and Recognition, Harbin Institute of Technology（哈尔滨工业大学深圳视觉目标检测与识别重点实验室）
ADPretrain: Advancing Industrial Anomaly Detection via Anomaly Representation Pretraining
School of Information Science and Electronic Engineering, Shanghai Jiao Tong University（上海交通大学信息科学与电子工程学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能MOE重点实验室）；College of Artificial Intelligence, Nanjing Agricultural University（南京农业大学人工智能学院）；Key Laboratory of Livestock Farming Equipment, Ministry of Agriculture and Rural Affairs, Nanjing Agricultural University（南京农业大学农业机械化关键实验室）
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
School of Computer Science & Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；School of Computing and Information Technology, Great Bay University（大亚湾大学计算机与信息科技学院）；College of Computer Science, Nankai University（南开大学计算机学院）；School of Artificial Intelligence, Shenzhen University（深圳大学人工智能学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）；National Engineering Laboratory of Big Data System Computing Technology, Shenzhen University（大数据系统计算技术国家工程实验室）
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning
Shanghai Jiao Tong University（上海交通大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
SurveillanceVQA-589K: A Benchmark for Comprehensive Surveillance Video-Language Understanding with Large Models
Department of Computer Science, Beijing University of Technology（北京理工大学计算机科学系）；Inspur Electronic Information Industry Co., Ltd（Inspur电子信息产业有限公司）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；JD Explore Academy（京东探索研究院）
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual Learning
State Key Laboratory of Precision Measurement Technology and Instruments, Department of Precision Instrument, Tsinghua University（清华大学精密测量技术与仪器国家重点实验室，精密仪器系）；State Key Laboratory of Intelligent Manufacturing Equipment and Technology, Huazhong University of Science and Technology（华中科技大学智能制造装备技术国家重点实验室）；Hunan University School of Robotics（湖南大学机器人学院）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches
Computer Science Department at the University of Warwick（沃里克大学计算机科学系）；University of Warwick（沃里克大学）；College of Computer Science, Sichuan University（四川大学计算机学院）
Learning Event Completeness for Weakly Supervised Video Anomaly Detection
School of Computer Science；Technology, Tongji University, Shanghai, China.；Department of R\&D Data, Microsoft Asia-Pacific Technology CO Ltd, Shanghai, China.
VisionGPT: LLM-Assisted Real-Time Anomaly Detection for Safe Visual Navigation
School of Computing（计算机学院）；Clemson University（克莱姆森大学）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Stevens Institute of Technology（史蒂文斯理工学院）；School of Cultural and Social Studies（文化与社会研究学院）；Tokai University（东海大学）
SurgCoT: Advancing Spatiotemporal Reasoning in Surgical Videos through a Chain-of-Thought Benchmark
School of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机科学与软件工程学院）；University of Nottingham（诺丁汉大学）；School of AI, Shenzhen University（深圳大学人工智能学院）；Guangdong Provincial Key Laboratory of Intelligent Information Processing, Shenzhen University（广东省智能信息处理重点实验室）
Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization
School of Mathematics and Statistics, Southwest University（数学与统计学学院，西南大学）；School of Mathematics and Statistics, Xi’an Jiaotong University（数学与统计学学院，西安交通大学）；School of Computer Science and Technology and Ministry of Education Key Lab For Intelligent Networks and Network Security, Xi’an Jiaotong University（计算机科学与技术学院和教育部智能网络与网络安全重点实验室，西安交通大学）；School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China（数学科学学院/图像与视觉计算研究中心，电子科技大学）；Institute of Methodologies for Environmental Analysis, CNR-IMAA（环境分析方法学研究所，CNR-IMAA）
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；Sun Yat-sen University（中山大学）；The Chinese University of Hong Kong（香港中文大学）
