Avigilon has added facial recognition technology to the latest version of its Avigilon Control Center (ACC) 7.4 video management software (VMS).
The new appearance alerts capability of ACC 7.4 will help security teams accelerate response times by identifying people of interest in enterprise settings. People of interest are identified based on a secure, controlled watch list created and maintained by authorized users at the commercial organization.
For organizations that use the new ACC software and license their Avigilon cameras for facial recognition, cameras will seek to identify potential matches based on the watch list. If a potential match is found, the user is alerted within the ACC software, and security personnel can then determine whether further investigation or action is necessary.
“Our latest ACC software delivers substantial benefits to our commercial customers by offering facial recognition technology in a secure and controlled manner,” states John Kedzierski, senior vice president, video security solutions, Motorola Solutions.
“The appearance alerts capability enables our customers to move from a reactive approach — staring at a wall of video feeds where critical information can be easily missed — to a proactive approach that brings important information directly to authorized users so they can make better-informed decisions.”
Kedzierski said the company views facial recognition as an aid that can improve the decision-making of the user.
“It does not make consequential decisions or initiate actions on its own,” he explained. “We refer to this approach as ‘human in the loop,’ and it is foundational to the way we apply AI.”
ACC’s new facial recognition capabilities balance use of artificial intelligence and privacy. For example, user authentication is required for these capabilities, audit logs of user actions are generated, data retention periods for the watch list can be specified within the application, and records can be expunged or deleted on demand as well as verified through auditing and reporting.
Data is locally hosted, owned and controlled by the business or school. The data used to train machine learning algorithms is also thoroughly evaluated, ensuring quantity, quality and diversity to ensure high accuracy and consistent performance.
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