For a few years now, AI has been one of the biggest trends in the security department. Providing the ability to learn with time, AI offers the technology to speculate and recognize potential issues as they are unlocking. Considering the video's value in security, it was a good fit for AI apps.
The AI's growth & development for video can be traced to the transformation of video analytics
in the past couple of years to the point where analytics & AI are so intermingled that it's tough to tell where one begins and the other ends. Irrespectively, the combination has a dramatic impact on the industry.
According to the director of sales, Greg Cortina, one instance of how video analytics has accelerated AI's advancement can be seen in face recognition transformation. He further adds that face recognition captures face pictures as snapshots & cannot recognize individuals. But once those pictures have been segmented as faces, face recognition can make positive IDs of individuals via deep learning algorithms.
As per Paul Garms, director of regional marketing, developments in video analytics's reliability have made ML possible. Machine learning is a part of narrow artificial intelligence and is a way of training an algorithm by feeding it data as a way of teaching it to adjust itself to enhance its performance.
To properly understand AI's role in video analytic for security uses, you must look at the end goal: to propel video operators' focus from unorganized, seemingly endless video streams towards informative metadata that stimulates action & produces intelligence. As per product manager Tom Hofer, this means taking advances in machine learning, computer vision, and pattern identification and embracing them in a security context that enables quick responses & amplifies human resources.
From Responsive to Driven
Conventionally, cameras were deployed as a hindrance with mixed outcomes. From there, cameras became forensic tools for probing incidents after the fact. And as video analytics have transformed, cameras & systems have shifted from forensic tools towards driven solutions. The mix of analytics & AI guarantees to push this transformation ever further, as per Walthers.
With AI's advancement, we've seen that you can more deeply and precisely recognize human behaviors & human characteristics to ascertain if something is an animal or there is a criminal in the vicinity, says Alex Walthers, business development manager. He further adds that video cameras can be trained much faster on tons of pictures with artificial intelligence to continue build upon and enhance.
Tasks such as line-crossing, people counting, & perimeter detection are a few narrow-focus repetitive tasks video analytics excel at performing, far better & more productively than a human could ever imagine.
Several repetitive tasks that would have been done by a person are now freed up so those experts can take the data that's produced and do something with it, such as interpret it, answer it, engage with it in some way, added Walthers.
AI is the latest stage video analytics have reached in their continuous evolution, says Florian Matusek, product group director. He further adds that machine learning is another level where you might say it was tracking objects 0-15 years back. Perhaps, that was a step further. These days, it's machine learning and being able to do the same things analytics did 15 years back much more precisely and really usable.
If video analytics moved the needle in enhancing security, AI would take that improvement much further, says senior vice president Sean Foley. He also adds that AI is really the revolutionary here. When it comes to asset protection tech, it will cause a significant shift in the tech type our clients are using, the solutions that we offer them, and the effectiveness level that they have in defeating all of the things that asset protection & loss prevention teams are dealing with on everyday bases, such as their client's and associates security.
The two significant factors that can create video analytics challenges are video quality & what appears in the camera's visual field. A lot of movement or low-quality video can make it tough for the analytics to ascertain what is exactly happening, causing false alerts. But AI has the power to change that by making analytics much more trustworthy, according to Aaron Saks, product & technical manager.
Irrespective of its ability to enhance video analytics, one of the issues with AI is the term itself; as per Florian Matusek,
AI means automation. Normally, while talking about AI to you, rather than AI, we say IA, i.e., intelligent automation. It automated stuff, and it does so very well by using deep learning and machine learning. However, referring to it as AI suggests that it is brilliant, also close to humans, which it isn't, says Matusek.
Video analytics or AI also has the power to affect the existing video deployments used for simple supervising operations, making them capable of identifying issues as they are unraveling & offering users with possible solutions quickly.
Jason Burrow, the regional sales director, says that, for years, cameras have supervised major highways & intersections and used easy analytic triggers. Now, we see the benefits of smart traffic flow systems, for instance, automating variable speed limitations and traffic light patterns to mitigate roads from turning gridlocked or notifying drivers to bottlenecks ahead.
Finally, we can expect AI users to continue to transform as merchants decentralized computation away from servers & the cloud to the network's edge. This means lower bandwidth needs, quicker alerts, and most essentially even reduced price points.
This is where AI combined with video analytics comes into the picture, delivering the kind of intelligence that lets businesses either act fast to prevent an incident or ideally cease it before it happens.
Folays further says that the future is bright. AI is helping us with the process, and it's just incredible what you can do with AI and where it's going.