VisionGuard: Explainable Deep Learning Framework for Real-Time Anomaly Detection in Surveillance Video
Jahnavi Somaraju, L. Mounika, M. Mounika, K. Mounika, BS. Karishma
Surveillance anomaly detection systems built around a single monolithic deep network are difficult to interpret, brittle to distribution shift, and offer operators no rationale on which to act. This paper presents VisionGuard, an explainable deep learning framework that reorganizes real-time video anomaly detection as a coordinated multi-agent system. A Perception Agent extracts spatio-temporal features, a Temporal Reasoning Agent scores short clips for anomalous dynamics, an Explanation Agent generates saliency-based and natural-language rationales, an Orchestrator Agent routes decisions and resolves conflicts, and an Alert and Response Agent manages operator-facing triage with a human-in-the-loop feedback channel. The agents communicate over a lightweight message bus and share state through a hybrid short-term feature store and long-term vector memory that supports retrieval-augmented context. On UCSD Ped2, CUHK Avenue and ShanghaiTech, VisionGuard achieves a frame-level AUC of 91.7%, an improvement of 5.6 points over the strongest baseline evaluated, while sustaining 29.4 FPS end-to-end on a single GPU. Ablation results show every agent and the sharedmemory/feedback mechanisms contribute measurably to accuracy, and qualitative case studies show the Explanation Agent's rationale is a substantive aid to operator decision-making.