Video analytics are in high demand at airports. With so many cameras to monitor, video surveillance that works with advanced video analytics to proactively identify potential threats is critical to the success of a security plan. Furthermore, these technologies can help operators identify the most critical information at any given time.
“Powered by sophisticated, computer-based algorithms, these advanced analytics incorporate neural networks and deep learning to imitate a human’s ability to recognize, allowing the technology to adapt to and learn from new situations,” said Alan Stoddard, VP and GM of Situational Intelligence Solutions at Verint. “These innovations open up new doors to airports because they can deliver the accuracy and scalability required to support advanced recognition in real-world environments with heavy traffic and diverse populations, rather than relying on one-to-one scenarios.”
Jumbi Edulbehram, Regional President of Americas at Oncam, pointed out how airports are a notoriously difficult environment for traditional analytics to work well. However, nowadays with machine learning-based analytics things are changing.
More accurate face recognition
The advancements in machine learning and artificial intelligence (AI) are enabling analytics like face recognition to deliver highly accurate, mission-critical security applications like airports.
“Greater accuracy in facial recognition is obviously very beneficial at airports where correct identification of passengers, staff, etc., is critical. Some airports/airlines have already started using facial recognition to identify passengers boarding flights, while others are using it as a biometric for access to restricted areas at the airport,” Edulbehram said.
Benjamin Low, VP of APAC at Milestone Systems, noted that face recognition is also being used as another form of authentication for border control, passport and security access; tracking of black- and white-list individuals; speedy clearance in congested places; and real-time matching.
How airports benefit from analytics on the edge
Video analytics at the edge allows every network video surveillance camera to be smart and understand what it sees. In an airport environment, this allows for operators, security staff and other users to be alerted to potential threats or situations the moment they happen.
Improved camera technology and advancements in analytics have made analytics at the edge a more viable option. At airports, edge analytics can, for example, detect counter flow on an escalator or a group forming in unusual places or a person or car near the perimeter fence.
“Built-in video analytics allows users to retrieve the right footage from hours of stored video instantly, analyze the scene by providing all kinds of statistics (metadata), and take appropriate action faster, easier and more efficiently,” said Wings.
“With built-in video analytics it is possible to set certain alarm rules, such as someone approaching or climbing over a fence, someone loitering at the parking lot, or objects left behind in a certain area like a box blocking the emergency exit,” he explained. “Operators in the control room will be alerted the moment one of these alarm rules is met.”
This is different to what can already be detected today. “When data of multiple cameras is aggregated statistics like counting metrics, crowd density and other information can be used to inform passengers about waiting times, or other relevant information beyond security,” he added.