Page 14 - Security Today, October 2018

Solving Problems Edge-based analytics may be the solution to the problem
BTy Alex Walthers
he industry is changing so rapidly that the following statement might seem bold but it’s true. No one wants to buy a camera. The customer
may need a camera, they may be tasked with retrofitting their entire security system with cameras, but what they’re really looking for is a solution to a problem. The camera is only one piece of the solution, which also in- cludes a VMS, storage, switches, cabling, etc.
There is no one camera that will fit ev- ery application. The customer may want to monitor a perimeter, a parking lot, cash reg- isters, or any number of other areas in their environment—each with its own unique set of variables that determine what the solution needs to be.
As we see camera specifications like mega- pixels and frames per second (FPS) continue to increase, it should be noted that there are limitations to how much performance can be squeezed onto a network. Due to bandwidth and storage limitations of an end user’s sur- veillance system, it would be difficult for cameras to transmit 100-megapixel video at 200 frames per second. To improve system performance, there are areas beyond these technologies to determine new functionality and add value to the edge.
One way to do that is through the use of video analytics. Analytics not only adds intelligence to cameras to make even better use of technologies like resolution and FPS, but they bring a more practical and effective value to the edge and this is when solutions are created.
Edge Applications
There are two ways video analytics can be deployed, either within the camera at the edge or at the server where video is stored. If we’re talking purely about bandwidth and storage savings, edge-based analytics make the most sense. Similar to a smartphone, these applications allow users to customize their systems and add specific functionality as needed. There’s also no wasted data. The camera in this scenario acts as a computer, processing the data and only sending the data you need across the network.
For example, rather than adding more cameras, servers or other hardware to gener- ate intelligence, users could add a perimeter analytic to a thermal camera or add a license plate recognition application to a parking lot camera. In both examples, it would allow
the customer to add more value to their so- lution and tailor the surveillance system to their needs. This now adds intelligence to the existing camera at their site and solves the customer’s problem all at once.
Bandwidth and Storage
Deploying analytics applications at the edge eliminates the need to send all the video over the network, which saves on both bandwidth and storage. Instead, all processing happens at the camera allowing just data and video clips to be sent to the head end. This can re- duce the complexity and cost of the system by avoiding the need to install additional servers and other connected hardware for processing and data transfer.
The alternative server-based approach would mean sending all video, 24/7, 365 days a year, to a central server where it must be processed and stored, eating up significant bandwidth and storage space. This type of deployment would likely require additional servers for each individual application. This is not only cost-prohibitive, especially for scenes where there isn’t a lot of activity, but this makes any event requiring investigation more complex and time consuming.
With edge-based applications, the only video that needs to be sent to the head end would be a short video clip following the activity that triggers the application. Some applications might only require data be sent across the network as well—without the ac- companying video. This substantially cuts down on the amount of bandwidth you’re sending across the network and less storage being taken up centrally.
Beyond Security
Another benefit of edge-based analytics ap- plications is the ability to easily utilize video for broader purposes that include, but extend beyond, traditional security. A good example of this would be a retail environment, where there are likely to be cameras installed at entrances throughout the store and above cash registers. Monitoring people coming in, whether they are stealing anything, and keeping an eye on POS transactions may all be done for security purposes. Edge-based analytics can be installed to create cross- functional video, expanding the use case for the video into other areas.
The camera at the front of the store could be configured with an application to collect and monitor demographic data (age, gender, etc.) to collect information on a store’s target audience and other operational data. Cam- eras can also employ heat mapping analytics to determine what areas of the store people tend to go to, how long they linger and other factors that could be used for marketing and merchandising. By combining demographic and store traffic information, a retailer could identify a prime location for a brand of product and then charge the manufacturer for that premium advertising space. The re- tailer could also use information gathered from edge-based analytics to change the con- figuration of the store to alleviate congestion in a particular area.
Existing cameras could also be used to improve customer service. Retailers are fo- cused on the customer experience and no one likes to wait in a long, slow-moving line. Cameras near checkout could run a queue-
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