This paper presents a concise review of video anomaly detection (VAD) and introduces a new dataset, Multi-Scenario Anomaly Detection (MSAD), to address the challenges in this field. VAD is crucial for applications such as security surveillance, traffic monitoring, and healthcare. Despite extensive research, there is a lack of comprehensive reviews that provide insights into current challenges and future directions. The paper examines models and datasets from various perspectives, emphasizing the critical relationship between model and dataset development. The quality and diversity of datasets significantly influence model performance, and dataset development must adapt to the evolving needs of emerging approaches. The paper identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, the authors introduce MSAD, which comprises 14 distinct scenarios captured from various camera views. The dataset includes diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. The authors 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. The paper also discusses the challenges in VAD, such as the limited scope of existing methods, poor generalizability, and vulnerability to various factors like reflection, illumination changes, and complex background environments. These challenges highlight the need for a high-quality, multi-scenario, and comprehensive dataset. However, existing benchmarks mostly focus on single-scenario datasets, human-related anomalies, and lack multi-scenario generalization abilities. The authors propose a lightweight review that offers several benefits: (i) it provides a quick reference for researchers and practitioners, making it easier to get up to speed on VAD without sifting through extensive details; (ii) it enhances accessibility for a broader audience, including newcomers to the field, by allowing a quick grasp of essential concepts; and (iii) it offers focused guidance by distilling critical information into clear, actionable insights. The paper introduces the MSAD dataset, which includes 35 human-related anomalies and 20 non-human-related anomalies. The dataset features 14 distinct scenarios, including roads, malls, parks, sidewalks, and more. It incorporates various objects like pedestrians, cars, trunks, and trains, along with dynamic environmental factors such as different lighting and weather conditions. The dataset is available for use and provides a comprehensive benchmark for real-world multi-scenario video anomaly detection. The paper also discusses the evaluation protocols for the MSAD dataset, including cross-view and cross-scenario evaluations. The results show that the MSAD dataset provides superior performance in both generalizability and adaptability. The dataset is designed to address the challenges of detecting anomalies across diverse and evolving surveillance scenarios, offering valuable resources and insights to advance the field of video anomaly detection.This paper presents a concise review of video anomaly detection (VAD) and introduces a new dataset, Multi-Scenario Anomaly Detection (MSAD), to address the challenges in this field. VAD is crucial for applications such as security surveillance, traffic monitoring, and healthcare. Despite extensive research, there is a lack of comprehensive reviews that provide insights into current challenges and future directions. The paper examines models and datasets from various perspectives, emphasizing the critical relationship between model and dataset development. The quality and diversity of datasets significantly influence model performance, and dataset development must adapt to the evolving needs of emerging approaches. The paper identifies practical issues, including the absence of comprehensive datasets with diverse scenarios. To address this, the authors introduce MSAD, which comprises 14 distinct scenarios captured from various camera views. The dataset includes diverse motion patterns and challenging variations, such as different lighting and weather conditions, providing a robust foundation for training superior models. The authors 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. The paper also discusses the challenges in VAD, such as the limited scope of existing methods, poor generalizability, and vulnerability to various factors like reflection, illumination changes, and complex background environments. These challenges highlight the need for a high-quality, multi-scenario, and comprehensive dataset. However, existing benchmarks mostly focus on single-scenario datasets, human-related anomalies, and lack multi-scenario generalization abilities. The authors propose a lightweight review that offers several benefits: (i) it provides a quick reference for researchers and practitioners, making it easier to get up to speed on VAD without sifting through extensive details; (ii) it enhances accessibility for a broader audience, including newcomers to the field, by allowing a quick grasp of essential concepts; and (iii) it offers focused guidance by distilling critical information into clear, actionable insights. The paper introduces the MSAD dataset, which includes 35 human-related anomalies and 20 non-human-related anomalies. The dataset features 14 distinct scenarios, including roads, malls, parks, sidewalks, and more. It incorporates various objects like pedestrians, cars, trunks, and trains, along with dynamic environmental factors such as different lighting and weather conditions. The dataset is available for use and provides a comprehensive benchmark for real-world multi-scenario video anomaly detection. The paper also discusses the evaluation protocols for the MSAD dataset, including cross-view and cross-scenario evaluations. The results show that the MSAD dataset provides superior performance in both generalizability and adaptability. The dataset is designed to address the challenges of detecting anomalies across diverse and evolving surveillance scenarios, offering valuable resources and insights to advance the field of video anomaly detection.