Semi-Siam: A Novel Intelligent Monitoring System With a Multibaseline Video Anomaly Detection

This article introduces a novel anomaly detector for intelligent monitoring systems, leveraging multiple assessment baselines, including conventional, frame-based, and scenario-based approaches, to enhance anomaly detection. The integration of these baselines improves detection accuracy and contextual understanding of anomalies. A key feature of the proposed methodology is the incorporation of the Semi-Siam technique, a semi-supervised few-shot learning approach, which significantly boosts performance in scenarios with limited training data. Extensive simulations on multiple datasets demonstrate the proposed system's effectiveness and substantial improvements over existing techniques. The results indicate that this methodology offers a robust and efficient solution for real-world video anomaly detection applications, such as the City of Calgary dataset, providing significant advancements in detection accuracy and adaptability.