Meta Learning Approach for Adaptive Anomaly Detection from Multi Scenario Video Surveillance

Video surveillance is widely used in different area like road, mall, education, industries, retail, park, Bus stand, Restaurant and smart cities, each presenting unique anomalies requiring specialized detection. However, adapting anomaly detection models to novel viewpoints within the same scenario poses challenges. Extending these models to entirely new scenarios necessitate retraining or fine-tuning, a process that can be time-consuming. To address these challenges, Model named Video Anomaly Detector Model has been proposed, leveraging the meta learning framework for faster adaptation using Swin Transformer for feature extraction to new concepts. In response, The Multi-Scenario Anomaly Detection (MSAD) dataset, featuring 14 scenarios from various camera views, is the first high-resolution anomaly detection dataset for real-world applications. It includes diverse motion patterns and challenging variations like different lighting and weather conditions, providing a strong foundation for training advanced models. Experiments confirm the effectiveness of MAML, particularly when trained on the MSAD dataset. MAML excels under new viewpoints within the same scenario and performs competitively in entirely new scenarios, showcasing its potential for detecting anomalies in diverse and evolving surveillance environments.