The 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 includes a VMS, storage, switches, cabling, etc.
There is no one camera that will fit every application. The customer may want to monitor a perimeter, a parking lot, cash registers, 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 megapixels 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 surveillance 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.
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 generate 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 solution 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.
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 reduce 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 accompanying video. This substantially cuts down on the amount of bandwidth you're sending across the network and less storage being taken up centrally.
Another benefit of edge-based analytics applications 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 crossfunctional 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. Cameras 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 retailer could also use information gathered from edge-based analytics to change the configuration of the store to alleviate congestion in a particular area.
Existing cameras could also be used to improve customer service. Retailers are focused on the customer experience and no one likes to wait in a long, slow-moving line. Cameras near checkout could run a queueline application that would alert management when lines are long, allowing them to open another register to clear the congestion.
These are just some of the many ways edge-based analytics applications can contribute to greater business intelligence. While a one percent reduction in shrink is a pretty good number, a one percent increase in sales is typically a much larger number. With this in mind, end users and integrators may have access to operational, marketing, advertising and other budgets beyond just security. Working with multiple departments to meet particular needs positions installers to offer more services, and more customization with edge-based video analytics, which helps create long-term customers and consistent revenue streams.
Initially, surveillance cameras were used mainly for deterrence, and they were bulky, so they could be seen easily. The thought being that highly visible cameras would deter or prevent someone from committing a crime or doing anything malicious. Under this model, video analytics significantly improved forensic viewing capabilities.
Today's cameras, however, trend toward being sleeker, more covert- type models. Many end users choose cameras that are more aesthetically pleasing, which doesn't do much for deterrence. Therefore, it's important for end users to take a more proactive approach to security with their video. We are now seeing a shift toward edge-based analytics applications providing the necessary intelligence to enable greater proactivity. For example, glass-break or audio aggression analytics can provide operators with the opportunity to intervene in a situation before it can escalate and become worse.
With the Internet of Things trend we are seeing in the security industry, other hardware technologies can also be complimentary. As more and more varied systems and devices are connected and integrated, systems are able to deliver even greater intelligence that provide operators, guards, first responders and others with valuable situational awareness to respond to threats or incidents most effectively.
The analytics market has experienced ups and downs over the years. Many applications oversold and underperformed which left both end users and integrators looking for more. There is a new level of maturity in the market that has realistic expectations and performance from these types of analytics. The time is ripe for security professionals to seek out edge-based video analytics applications—and begin testing them and becoming comfortable with them. These solutions provide end users with the ability to customize cameras to meet their particular requirements, remove bandwidth and storage limitations, and allow for relatively quick adaptation when video requirements change.
Additionally, customers can take advantage of applications that help them increase the return on their video surveillance investment while taking a more proactive approach to security. Remember that an IP camera is essentially an intelligent network device that happens to have a lens, so with all the benefits analytics applications deliver, it just makes sense to harness that processing power to offload video processing from the server to the edge.