Towards a resource efficient and privacy-preserving framework for campus-wide video analytics-based applications

Abstract

Video surveillance and analytics solutions based on Artificial Intelligence (AI) are increasingly being deployed across industries, including academia. There are a number of use-cases for campus-wide video analytics applications. Detecting events of interest in real-time and generating alerts is a core requirement for such applications, making them both network and compute intensive. Thus, the underlying framework needs to be resource optimized in terms of latency, compute and storage requirements for a multitude of video applications. Increasingly privacy concerns have been voiced against the pervasive deployment of video analytics-based applications. Thus, protecting the privacy of students and staff in a campus setting shall be a major design consideration for such systems going forward. This paper presents a resource optimized and privacy preserving framework for campus-wide video analytics applications. Several use-cases are presented and early results from the deployment of the proposed framework establish its feasibility and effectiveness.