Privacy-Aware Object Representation for Surveillance Systems

Abstract

Real-time object tracking, feature assessment and classification based on video are an enabling technology for improving situation awareness of human operators as well as for automated recognition of critical situations. To bridge the gap between video signal-processing output and spatio-temporal analysis of object behavior at the semantic level, a generic and sensor-independent object representation is necessary. However, in the case of public and corporate video surveillance, centralized storage of aggregated data leads to privacy violations. This article explains how a centralized object representation, complying with the Fair Information Practice Principles (FIP) privacy constraints, can be implemented for a video surveillance system.