Page 12 - Security Today, October 2020
P. 12

“Application developers will continue striving for analytics able to detect and evaluate ever more subtle nuances in behavior and the environment.”
images in shadow and bright sunlight and variable weather condi- tions, images with facial hair, hats, glasses, tattoos and other distin- guishing differences. And if the application comes across a novel image for which it has no data points it can reference it could fail to recognize the image as a face.
That’s not to say significant strides have been made since the early days of video analytics. Take, for example, video motion detection. We’ve come a long way from simply detecting pixel- changes in the scene. Today’s motion detection analytics have been designed to recognize patterns. They’re able to filter out non-essen- tial data like shadows, passing objects like cars and branches, the bloom from a headlight, even birds – which leads to significantly fewer false positive alerts.
Other video analytics such as license plate recognition and ob- ject classification (like type of car, color, make and model), have also grown in sophistication over the years with the ability to ac- curately discern and transmit essential data and ignore anything irrelevant to the specific task at hand.
The video analytics industry has burgeoned into a massive eco- system of problem-solving tools. But to achieve more predictive intelligence, many of these algorithms rely on larger datasets of in- formation and greater processing power to reach an acceptable level of accuracy. This has led many businesses to realize that computing power and datasets at the edge are the core and insufficient to the task. So, they’re turning to a third option for their analytics op- erations: cloud computing. Using a cloud computing service offers certain advantages that neither the edge nor the core can provide:
Great scalability. Going to a cloud computing model offers al- most unlimited processing power and gives users access to large data sets and images to train video analytics algorithms to targeted tasks.
Great flexibility. Cloud computing service is an elastic solution. Businesses only use provider resources on an as needed basis.
Lower upfront investment. Businesses don’t have to purchase, maintain and update local server resources, which makes it pos- sible for companies with fewer financial means to access virtually unlimited advanced hardware and software resources without a huge capital investment. They can employ video analytics as a ser- vice and allocate the expense to their operating budget.
In addition to ever greater accuracy, one of the reasons that video analytics are gaining traction is that many of the newer algorithms are hardware agnostic. In the beginning, manufac- turers only allowed analytics created by their own in-house soft- ware development team to be embedded on their cameras. As the demand for customized solutions grew, manufacturers gradu- ally began opening their products to third-party developers. But, there was a caveat. For the applications to run on those cameras, these outside developers had to use the manufacturer’s own pro- prietary application development tools and platform. With few
exceptions, this generally constrained an application’s usefulness to a single manufacturer’s product line.
With the rise in the Internet of Things and best-of-breed, mixed- vendor ecosystems, this position was no longer sustainable as it was limiting users’ ability to grow their systems. Today there’s a big push for open source development tools based on industry standard ap- plication programming interfaces. The goal would be to create a common development framework that would support deploying the video analytic to multiple tiers. In other words, any analytics software written within this framework would be interoperable with edge devices, on-premise servers, or cloud computing farms.
The other rationale for taking this open source approach would be give developers access to a vast library of proven computer vi- sion and machine learning software on which to build their source code. This would dramatically speed up software development and drive innovation, which would increase the value of all manufac- turers’ cameras.
Many of the video analytics developed for surveillance and security have, over time, found their way into business operations, especially retail and healthcare. For instance, loitering analytics are being used in stores to detect possible shoplifting or a custom- er needing help from service staff. In fact, some retailers are tying the video analytics into intelligent audio systems to trigger a mes- sage to the customer that assistance is on the way. This has proven to be a great deterrent against theft and lost sales opportunities.
In healthcare, some hospitals are using crossline detection analytics to trigger alerts when patients wandering or try getting out of bed without assistance. Some hospitals are augmenting their video analytics with audio analytics (such as aggression and gunshot detection) and public address systems to reduce work- place violence.
As result of the COVID-19 pandemic, many establishments are finding novel ways to employ their video analytics. Facial rec- ognition software is being modified to detect whether people are wearing masks to ensure compliance with health and safety pro- tocols. Occupancy analytics are deployed to alert management when the designated capacity is reached by current municipal codes. And many more innovations are in the pipeline.
As you can see, video analytics has come a long way since sim- ple pixel change detection. Software developers are designing them as multi-tiered solutions that can run at the edge, in the core and up in the cloud, giving users the flexibility to deploy and manage their analytics wherever they are best suited and most economical. They are using open sourced tools to construct these applications to be hardware agnostic, giving users the freedom to choose best of breed components for their installations.
Going forward, application developers will continue striving for analytics able to detect and evaluate ever more subtle nuances in behavior and the environment. This goal will be achieved by build- ing on the legacy of their predecessors and harnessing the power of AI and machine learning. This will lead to more
accurate and predictive performances that can help customers meet the daily challenges they face in their security and business operations.
Robert Muehlbauer is a senior manager of business development at Axis Communications.

   10   11   12   13   14