Security Business

MAR 2019

Find news and information for the executive corporate security director, CSO, facility manager and assets protection manager on issues of policy, products, incidents, risk management, threat assessments and preparedness.

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42 Security Business / / March 2019 PSA Security Network is work- ing diligently to help its integrators transition into a world in which sub- scription-based services and RMR are foundational elements. PSA recently introduced its Managed Security Service Provider (MSSP) program, designed to help systems integrators diversify their service offerings and realize the full potential and benefits of a managed services business model. AI is poised to be the game-chang- ing technology that helps advance that transition for integrators. It has been an uphill journey because of the mismatch between 20th century security industry practices and the low number of as-a-service offerings available; however, reaching the summit where AI and RMR meet is within sight. ■ » Ray Bernard, PSP CHS-III, is the principal consultant for Ray Bernard Consulting Services (www., a firm that provides security consulting services for public and private facilities. In 2018 IFSEC Global listed him as No. 12 in the world's Top 30 Security Thought Leaders. He is the author of Security Technology Convergence Insights, available on Amazon, and is an active member of the ASIS member councils for Physical Security and IT Security. Follow him on Twitter, @RayBernardRBCS. For bicycle detection, let's say that the layer for "detected object parts" identifies wheels, handlebars, frame and cyclist in the image. e layer for "classify object parts" differentiates between a bicycle wheel and a motor cycle wheel. Based on all the analysis, the layer for "classify object" eventu- ally concludes the object is a bicycle and not a motorcycle. How many hidden layers there are depends on how challenging the var- ious steps to object recognition are and how much soware is required for each step. What if a cyclist's back- pack must be detected? What about a second rider on the bicycle? Do colors matter? Does object speed matter? Recent advances in deep learning have made significant improvements in video analytics accuracy. For exam- ple, in people-counting applications where the machine accuracy ranged between 80 and 90 percent, deep learning improvements have brought that accuracy up to 98 percent or bet- ter. e greater the accuracy, the more complex the deep learning is – and the more computing power it requires. Higher accuracy also means higher cost; fortunately, not all applications require 98 percent or better accuracy. Cameras on a Mission Current AI research by Milestone Sys- tems is applying context in a different way – using deep learning to automat- ically adjust camera configuration in real time to optimize video settings based on the camera's purpose. At the 2019 annual Milestone Inte- gration Platform Symposium (MIPS) event, about six minutes into his Day 1 presentation, Barry Norton, Mile- stone's Director of Research, provided example video for a camera whose purpose is to perform license plate recognition (LPR). e demonstration used two Canon VB-S900F cameras, both initially con- figured optimally for best general per- formance using on-camera settings. en one of the cameras activated server-based AI to constantly adjust for best contrast, lack of glare, and lack of motion blur – creating a star- tling difference between two versions of the same low-light scene. Obtain- ing this level of camera performance around the clock is not possible with on-camera configuration settings. AI and RMR: Perfect Partners Cities are already deploying AI tech- nologies for public safety and security. In Stanford's 2016 AI Index report, the authors concluded that by 2030, the typical North American city will rely heavily on AI technologies, including cameras. Although not all types of AI perform as well as others, there are AI-based quality improvements that make AI camera analytics much more effective than ever. Such analytics also apply beyond smart city use-cases into many business and industry sectors. AI-based technologies are typ- ically offered under an "as-a-ser- vice" monthly-subscription model – whether the AI computing is done on the cloud or on premises. Typically, that delivery model results in custom- ers expanding their subscriptions year over year due to the increasing value of the new features. Check out the difference in an LPR camera image without (top) and with (below) server-based AI that constantly adjusts for best contrast, lack of glare, and lack of motion blur. Cover Story Photo: Milestone Systems

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