Thought Leaders Examine the Future of AI in Security, How It Can Be Utilized
Leading providers, plus a forward-thinking integrator, offer intel into customer benefits, use cases and concerns of artificial intelligence in the security industry.
Artificial intelligence (AI) and deep learning technologies are coming out of the shadows of engineering labs and military applications and finding their way into consumer and commercial security deployments.
With the ability to tap into other information facilitated by sensors, such as people counting, identity management and heat monitoring, an AI system can provide end users infinitely more information than traditional security applications ever could.
But despite the high hopes and sometimes dashed expectations, AI systems are still very much a foreign term to the security market. A more educated and realistic understanding of AI capabilities will only be made possible by the benefits of time and experience.
One thing it doesn’t take advanced analytics to figure out: AI technologies will only continue to evolve and take a predominant place in the video surveillance market.
A recent report from Research and Markets notes that AI will be a key driver of growth in the global physical security market. It projects a CAGR of 7.3% from 2018 to 2023. In addition, AI video analytics, according to Memoori, are expected to propel growth in the video surveillance market and see a CAGR of 13.4% between now and 2023.
Here, thought leaders offer insights on the AI landscape and speak to the latest developments, security and safety applications, early adopter markets, longer term projections, systems integrator opportunities, and overall challenges.
Advanced Video Analytics Highlight Latest Developments
Jake Cmarada, senior business development, enterprise sales for video surveillance camera maker Dahua, says that continued advancements in facial recognition, perimeter detection and heat map analysis are among the latest AI developments.
Particularly pertinent to facial recognition, the ability to house enormous image libraries and process from edge devices is now the trend. Additionally, deep learning models on the server side allow for data collection and ability to access data in real-time to make safer, smarter and more efficient sites, Cmarada says.
Dr. Sean Lawlor, data scientist at VMS provider Genetec, suggests that the most significant advances are coming out are video analytics. “We’re collecting so much data in video. It’s massive volume and chewing through it is tough. Machine learning and AI and video analytics are giving a lot of insight with minimal effort faster and more efficiently.”
Realistically, in security, we can now only see examples of machine learning, states Tim Palmquist, vice president Americas at VMS provider Milestone Systems. “Trained algorithms combined with new compute capabilities are making the long-awaited promise of reliable and productive video analytics come to life and become practical in day-to-day use,” he says, adding we don’t yet see commercial security examples of software that can learn and evolve being utilized. “We should expect, however, that these types of applications will soon make their way into our marketplace and, in turn, unlock a whole new chapter of innovation and opportunity.”
End Users Reap Greater Business Intelligence
Some of your potential security customers are already experiencing how AI can impact their bottom line in other ways. Learning customer demographics for retail vertical applications, for example, to analyze the purchase data, gender, age and interests to provide related merchandise/products promotion is among one of the most common use cases Cmarada points to.
“AI-enabled software delivers insights and intelligence that streamline business processes and provide business intelligence to improve the whole operation and grow the business.”
There are many cases where complex algorithms can automate otherwise manual processes, but historically there was not enough compute capability to effectively deliver results, Pamquist contends.
“Recent advancement in GPU and CPU have begun to resolve the compute issue, making machine learning more practical in day to day use cases,” he says. “Today we see video analytic solutions that work very well. Color, direction, correlation, object identification, facial recognition, synopsis, etc. are all examples that we see successfully in use today.”
Object recognition, pattern recognition, anomaly detection, predictive analysis are some of the more applicable use cases Jack Wu, co-founder and CEO of drone system integration specialist Nightingale Security (see their profile from last year’s Robosecurity issue here), cites for AI in physical security. While there are others, Wu predicts these to become very popular soon. “Currently, we’re seeing object detection as a pervasive trend,” says Wu. “Anomaly detection will increase in accuracy as object detection becomes more mature and less false positives are being recorded. In the near future, rule-based task automation will become the standard and lead to autonomous patrolling, autonomous incident reaction and on-board decision making. Those features will lead to robots being able to monitor and react to incidents in real-time with humans brought into the loop as the robot’s performing identification, tracking and following.”
Large industrial customers and critical infrastructures as well as the military rank among the earliest AI adopters, Wu believes, due to mission-critical facilities protection that, if left unsecure, can have large financial, political and national security consequences. Retail, utilities and government agencies are also among the early adopter crowd to target, according to Cmarada.
Taking it from another perspective, Palmquist notes, “In our industry, video analytics is the obvious early adopter of machine learning. Consequently, they will also likely be the first forward with learning software that better meets the definition of artificial intelligence. Outside our industry, we can see that autonomous driving capabilities — machine learning augmented by LIDAR [light detection and ranging] — is an-other early mover.”
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