Edge Analytics: the opportunities for video surveillance
Future-proofing video surveillance
You’ve probably heard of ‘edge analytics’ (or ‘analytics on the edge’) in relation to video surveillance. Put in its simplest form, it relates to increasingly powerful video analytics taking place on the ‘edge’ of the network, i.e. in the camera itself.
As with any new technology that offers significant potential, the industry has enthusiastically grabbed onto the term. And it’s no surprise, when some of the immediate benefits of edge-based analytics versus server-based are considered.
Analytics taking place within the camera, on the edge of the network, means that only the valuable data needs to be transferred to the operator. Conversely, analytics taking place on the server requires that all of the data from the camera is transferred to the data center for analysis, and with that comes a much greater need for costly bandwidth. Secondly, analyzing video within the camera and as close as possible to its capture means that the images being reviewed are of the highest possible quality: there is no degradation that can come through compressing images prior to transfer (which is often done to reduce the former issue, ironically).
Today’s understanding of edge analytics vs. tomorrow’s potential
To take an example from traffic management, one of the issues with traditional analytics is the number of false alarms; for instance, cameras mistaking puddles or shadows for vehicles stationary on highways and creating alerts.
The greater accuracy in edge analytics reduces these significantly, but goes much further. Edge analytics brings with it the ability to distinguish between different types of object. Back on the road, trucks, buses, cars, and motorcycles can all be individually identified, creating huge efficiencies in traffic management, and opening new opportunities.
While applications like this are obviously beneficial, they only scratch the surface of the potential for edge analytics. Today’s use cases are still largely focused on what we call ‘scene analytics’, i.e. live viewing of a specific scene, analyzing what’s taking place, and responding with alerts or automatically triggered actions (for example, road sign warnings and traffic controls).
Uncovering what you don’t know you don’t know
The combination of data and metadata created by edge analytics can be hugely useful helping analyze enormous amounts of information collected over time. This will help organizations gain insights into areas of interest, what we might refer to as ‘what they know they don’t know’.
For instance (and simply), ‘how many times have cars blocked bus lanes in the past month?’ or ‘what’s the average number of people entering this metro station between 7.00am and 9.00am on a weekday morning?’ They don’t know the answer, but they know what they’re looking for.
While this capability moves the benefits of edge analytics forward once more, possibly the greatest value will come through the ‘unknown unknowns’, when analytics starts delivering insights into what you didn’t know you didn’t know.
This is where the true potential of edge analytics in video surveillance lies; the analysis over time of vast amounts of data, leading to the identification of patterns and their anomalies, and enabling as yet unforeseen improvements in safety, security, service delivery and efficiency, process optimization…the list goes on.
See full article from Axis.