With the rapid advancement of urbanization and the increasing density of population, urban public safety has become a critical focus of modern city governance. The integration of the Internet of Things (IoT) and Artificial Intelligence (AI) provides an innovative pathway for constructing intelligent public safety surveillance systems. This paper proposes a multi-layer urban surveillance framework that integrates an enhanced multi-label image recognition model, an adaptive pan-tilt camera positioning mechanism, and a collaborative cloud–edge control platform. To address challenges such as multi-label dependencies, real-time video segmentation, and heterogeneous system coordination, we design a hybrid architecture that combines a Graph Convolutional Network (ML-GCN), a Slice Recurrent Neural Network (SRNN), and convolutional visual features to model spatial-semantic dependencies in surveillance images. Furthermore, a real-time video localization and smart tracking system is implemented by incorporating task-driven frame classification, temporal filtering, and quadrant-based pan-tilt control guided by image block offsets. On the system level, we construct a cloud–edge integrated platform using Layer2/Layer3/SAN interconnection and deploy AI-based scheduling to manage distributed computational resources under dynamic constraints. Extensive experiments on urban surveillance datasets demonstrate that our method significantly outperforms traditional models in accuracy (Formula: see text9.3%), micro-Formula: see text (Formula: see text10.4%), and frame-level anomaly detection delay (reduced by 74.7%). Moreover, the cloud–edge scheduling model reduces average processing latency by 69.7% and increases task throughput by 90.1% compared with baseline strategies.
Yuting Zhao (2026) studied this question.