Improving Entrances with AI

Because security entrances do not have AI built into their technology, integrating intelligence into secured entrances requires a collaborative effort

• By Kurt Measom
• Dec 02, 2020

With the evolving dynamics of cloud storage and the ability to harness and proactively employ an ever-increasing pool of big data, AI in the form of machine learning and deep learning has become a disruptive technological force in the physical security industry. Advanced AI and low-cost network resources have significantly impacted video surveillance, which has been among the biggest beneficiaries of faster processing and impactful analytics. Building automation, fire systems, intrusion detection, and physical and network access control are all starting to incorporate AI functionality.

Finding the Right Balance with an AI Integration

But can AI play a role in making exterior and interior entrances more secure? Can AI improve system functionalities, including: distinguishing people from objects at a facility perimeter and interior entrances; deter piggybacking; spot and analyze potentially lethal objects and dangerous people; and help define secure areas in and around buildings creating a more defensive risk posture for an organization?

As we move to greater converged technology at the edges of connected systems, we face the challenge of how AI may practically support entry solutions such as security revolving doors, turnstiles, and swing doors. A disconnect between the objectives of the building owner and building code regulations can further complicate the security blueprint.

Consultant Ben Butchko, president and CEO of Butchko, Inc. and a former security engineer with ExxonMobil, warns that manufacturers’ goals must align with their end users’ needs when it comes to driving the development of embedded solutions with advanced sensors (cameras, microwave, LIDAR), operational analytics (facial recognition, tracking, object discrimination, pattern recognition), and active response (entry lockout, alert noti- fication) that can be implemented now to prevent or deter unauthorized entry.

“The security entrance must be part of the general building operations design, clearly separated from an architect’s complete authority. Most secured entries are specified in CSI Division 28, outside of building design since it is structural and falls under code compliance surrounding emergency egress as well as building capacity and throughput. Therefore, if this is to work, the rules for design and the merging of Division 28 and Division 8 must become refined, practical, and widely accepted,” Butchko said.

Because security entrances do not have AI built into their technology, integrating intelligence into secured entrances requires a collaborative effort with a thirdparty solutions provider. Video analytics are increasingly deployed to address use cases such as people detection, piggybacking, dangerous object detection and facial recognition among other issues relevant to secured entrances. The increased integration of AI providers with traditional security entrance partners has resulted in improvements, such as price, speed, ease of use and usability. It also includes the use of machine learning to improve algorithms over traditional modeling and correlation approaches, and integration with other systems and sensors.

The Solution Requires a Plan

Security entrances and mantrap portals often combine a number of systems, sensors and requirements. Portals by their nature are an integrated solution combining access control, video surveillance, mechanical hardware, sensors and design.

The addition of cameras to high risk portals has been an early example of this integration trend, enabling managers to be able to tie what took place at an entrance to a corresponding alarm condition such as a forced or jammed (propped) entrance/ exit. This capability can be further enhanced by analytics - for example, facial recognition could be used to determine which individuals might have set off the alarm condition. Analytics and other sensors could count the number of people that move through a portal during rush periods in “open” mode and also determine that a crowd has gathered and more doors/portals need to be opened to address the burst in demand for ingress or egress.

“From a design perspective there is an increasing demand, due to COVID-19, for touchless access. In this case, the integration of technologies and the use of machine learning can be leveraged to provide efficient, safe and secure access. Machine learning and AI are well adapted to leveraging data sets and, over time, gaining an understanding of conditions and matching them to access control and individual requirements,” said Salvatore D’Agostino, the CEO of IDmachines.

D’Agostino sees the convergence of AI into security spaces, not known for their reliance on analytic data, reshaping the landscape. AI can be used as a proactive step against intrusion at a security entrance like a swing door or turnstile and integrated into the access control and video security systems to provide rich analytics and situational awareness.

Emphasizing “What is Going to Happen”

“It has long been known that there are often patterns to human, and to the same extent, enterprise behavior. Access control, surveillance, and intrusion detection systems collect large amounts of data that is often stored and then deleted without much analysis.

Enterprises are now more attuned to the ability to leverage this ‘big’ data. These are evolving now to common data formats, real-time analytics and predictive tools. There seems like there would be a similar evolution in the capabilities of physical security systems where it is not so much what is happening at a turnstile, swing door or entryway, but what is going to happen,” D’Agostino said. “This would leverage the existing systems, sensors and data collection capabilities and use big data, and analytics to drive management and monitoring. The more that physical security systems adopt standard data types, sets and structures (using syslog for logging is a simple example) and the more intelligent these systems become, the more intelligence can be put into the predictive analytics.”

This article originally appeared in the November / December 2020 issue of Security Today.