Community Security with Low-Cost AI Cameras: Scenario-Based Applications and Trends in Edge Computing and Lightweight Models

Abstract

With the refinement of grassroots community governance, the demand for low-cost, wide-coverage video surveillance tools in community security continues to grow. Using the M-brand AI-enabled camera, which integrates edge computing and AI model lightweighting technologies, as a practical case study, this paper addresses core needs such as monitoring special groups and identifying fire hazards. It analyzes the technical requirements, application scenarios (e.g., equipment hazard identification, fire lane monitoring), and commercial advantages (cost, installation, efficiency, customer stickiness, value-added services) of low-cost video surveillance tools, considering the characteristics of grassroots governance—budget constraints, data sensitivity, and system interoperability. The paper also explores commercial challenges related to performance compatibility, cost controllability, data security, and system integration. Furthermore, it predicts future development trends across four dimensions: intelligence, collaboration, low-cost high performance, and policy drive. The study concludes that such tools must focus on "functional adaptability, cost controllability, and compliance/security" to address key pain points. In the future, they are expected to evolve from edge-assisted products into mainstream solutions for community security, providing technical support for refined grassroots emergency management and governance. Enterprises that align with these trends will gain a market advantage.