QOE-based intelligent intrusion detection Use case: University video surveillance

Abstract

A smart university is supposed to be a safe university. At this moment we observe multiple cameras in different locations in the Hall University and rooms to detect suspicious behavior such as violation, larceny or persons in a state of alcohol or drug intoxication. Samples of the video footage is monitored 24/7 by operators in control rooms. Currently the recorded videos are visual assessed after a suspicious event has occurred. There is a requirement for real-time surveillance with smart cameras which can detect, track and analyze suspicious behavior over place and time. The expanding number of cameras requires an enormous measure of observing operators. This paper proposes a distributed intelligent surveillance system based on smart cameras. We seek to improve the Quality of Experience QoE operator side or QoE-video surveillance expressed in function of i- resource availability constraints, ii- false detection of suspicious behavior, iii- define an optimal perimeter for intrusion detection (subset of cameras, network parameters required …).