Page 28 - Security Today, November/December 2020

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“Access control, surveillance, and intrusion detection systems collect large amounts of data that is often stored and then deleted without much analysis.”
become refined, practical, and widely ac- cepted,” Butchko said.
Because security entrances do not have AI built into their technology, integrat- ing intelligence into secured entrances requires a collaborative effort with a third- party solutions provider. Video analytics are increasingly deployed to address use cases such as people detection, piggyback- ing, dangerous object detection and facial recognition among other issues relevant to secured entrances. The increased inte- gration 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 algo- rithms over traditional modeling and cor- relation approaches, and integration with other systems and sensors.
The Solution Requires a Plan
Security entrances and mantrap por- tals often combine a number of systems, sensors and requirements. Portals by their nature are an integrated solution combin- ing access control, video surveillance, me- chanical 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 en- hanced by analytics - for example, facial recognition could be used to determine which individuals might have set off the alarm condition. Analytics and other sen- sors 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/por- tals 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 integra- tion of technologies and the use of ma- chine learning can be leveraged to provide efficient, safe and secure access. Machine learning and AI are well adapted to lever- aging data sets and, over time, gaining an understanding of conditions and match- ing 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 en- trance like a swing door or turnstile and integrated into the access control and vid- eo security systems to provide rich analyt- ics 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 con- trol, 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 se- curity 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 col- lection capabilities and use big data, and analytics to drive management and moni- toring. The more that physical security sys- tems adopt standard data types, sets and structures (using syslog for logging is a sim- ple example) and the more intelligent these systems become, the more intelligence can be put into the predictive
analytics.”
Kurt Measom is the vice president of technology and product support at Boon Edam.
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