AI-Based Smart Weapon Detection and Threat Assessment in CCTV Surveillance: A Review

Abstract

Closed-circuit television (CCTV) networks now produce far more footage than human operators can watch, and delayed recognition of an armed person can cost lives. This paper reviews AI-based approaches to weapon detection and threat assessment in CCTV surveillance. Four recent studies are examined in detail: an edge-deployable weapon and threat detector built on a lightweight CNN and Mask R-CNN, an edge–cloud collaborative framework for real-time object detection, a hierarchical context-tree approach to anomaly detection, and a review of deep-learning benchmarks for video surveillance anomaly detection. The studies are compared on architecture, datasets, hardware, evaluation metrics and reported results. The review shows that object detectors reach high accuracy on curated weapon classes, but small and imbalanced datasets, occlusion, false alarms, limited edge compute and the lack of a context-aware risk-scoring layer still prevent dependable deployment. A conceptual layered pipeline that joins edge detection, location-aware anomaly scoring and cloud-assisted model updates is outlined as a direction for future work.