Video Intelligence as a component of a Global Security system

TL;DR

This paper discusses the development of an intelligent, hybrid video surveillance platform integrated into a global security system, utilizing machine learning and knowledge management for enhanced urban security and operational support.

Contribution

It introduces a novel hybrid platform for video intelligence with a security ontology and common language for human-machine interaction, advancing urban surveillance capabilities.

Findings

Enhanced real-time monitoring and evidence retrieval

Improved predictive capabilities for security threats

Facilitated knowledge sharing among security operators

Abstract

This paper describes the evolution of our research from video analytics to a global security system with focus on the video surveillance component. Indeed video surveillance has evolved from a commodity security tool up to the most efficient way of tracking perpetrators when terrorism hits our modern urban centers. As number of cameras soars, one could expect the system to leverage the huge amount of data carried through the video streams to provide fast access to video evidences, actionable intelligence for monitoring real-time events and enabling predictive capacities to assist operators in their surveillance tasks. This research explores a hybrid platform for video intelligence capture, automated data extraction, supervised Machine Learning for intelligently assisted urban video surveillance; Extension to other components of a global security system are discussed. Applying Knowledge Management principles in this research helps with deep problem understanding and facilitates the implementation of efficient information and experience sharing decision support systems providing assistance to people on the field as well as in operations centers. The originality of this work is also the creation of "common" human-machine and machine to machine language and a security ontology.