Emerging Trends in Anomaly Detection for Surveillance Videos: Methods and Insights
J. Anju, M. Abdul Rahiman · 2025
Anomaly detection in video surveillance is an advanced field garnering increasing attention from the research community due to its critical role in ensuring public security. The demand for intelligent systems capable of automatically detecting anomalous events in streaming videos has led to the development of numerous innovative approaches. Researchers have explored diverse domains of anomaly detection, including network anomaly detection, financial fraud detection, and human behavioral analysis. In the context of video surveillance, this survey synthesizes recent advancements in methodologies, datasets, and results while critically analyzing their limitations. Some of the methods that are emphasized are unified frameworks that combine pose, object, and motion features, weakly supervised spatio-temporal models, and hybrid architectures that use both graph neural networks and diffusion models. These methods have shown ability on benchmark datasets such as UCF-Crime, ShanghaiTech, and UCSD PED1, achieving high AUC scores and enhanced anomaly localization. However, challenges such as adaptability to dynamic environments, reliance on extensive datasets, and real-time processing constraints persist. This review provides a comprehensive overview of the state of the art, offering insights into existing gaps and future research directions to advance video anomaly detection technologies for real-world applications.