Video Intelligence as a component of a Global Security system
This work addresses the need for efficient video analytics in global security systems, particularly for urban surveillance, but it appears incremental as it builds on existing technologies with extensions discussed.
The paper tackles the challenge of leveraging vast video surveillance data for security by proposing a hybrid platform that integrates video intelligence capture, automated data extraction, and supervised machine learning to provide fast access to evidence, real-time monitoring, and predictive capabilities.
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.