Cross-Contextual Vision: Architecting Privacy-Preserving Multi-Modal AI Systems for Public Safety and Retail in Mixed Spaces

Abstract

The development of privacy-preserving computer vision systems using mixed-use environments will have to play this critical role. It suggests an innovative single framework alongside adaptive privacy protection based on the situational context, whilst the capability of the functionality traverses across the spectrum. The layered design makes it possible to regulate the implementation of privacy policies, detection of context, and analysis functions to achieve complex and advanced functions, such as cross-camera tracking and behaviour analysis, without violation of the right to privacy. Non-biometric tracking approaches, context-aware model switching, and policy observability are technical methods. The framework contends with the main issues of context boundary detection, performance optimisation, and cross-system integration using multi-modal sensing, edge computing, and a privacy standardised interface. A case study illustrates its effective realisation in a mixed-use urban centre and shows the framework is capable of mediating privacy through contextual transitions even when an emergency situation occurs. The article also points out that any form of governance cannot be effective without technical steps and human supervision, consultation with stakeholders, and even reporting of its proceedings.