Advancing Video Anomaly Detection: A Concise Review and a New Dataset
This work addresses the need for diverse datasets in video anomaly detection for security and monitoring applications, but it is incremental as it primarily adds a new dataset.
The paper presents a concise review of video anomaly detection and introduces a new dataset, Multi-Scenario Anomaly Detection (MSAD), with 14 distinct scenarios to address the lack of comprehensive datasets, showing its potential for training better models.
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/]