End-user security perceptions in AI-enhanced surveillance platforms: A study of system integration and device performance

AI-enhanced surveillance platforms face significant challenges balancing computational demands with user experience requirements. The integration of artificial intelligence capabilities introduces complex performance constraints that impact power efficiency, thermal management, and alert accuracy while simultaneously creating perceptual challenges for end-users. This document examines the intricate relationship between technical performance metrics and user security perceptions in modern surveillance systems. Through comprehensive evaluation of device performance characteristics, alert accuracy patterns, and user interaction behaviors, key optimization opportunities emerge at the intersection of technical and human factors. The findings reveal critical thresholds in false positive rates that significantly impact user trust and engagement, alongside surprising paradoxes in how security confidence relates to system behavior. Environmental factors substantially influence both technical performance and user perception, necessitating adaptive approaches to resource allocation and interface design. By identifying specific patterns in alert management, trust development, and interface interaction, this document establishes a foundation for creating surveillance systems that effectively balance technical optimization with user-centered design, ultimately enhancing both objective security capabilities and subjective security confidence among users.