Video analytics as a security technology has been hyped for the past several years, with the widespread view that it has been oversold, overpriced, misapplied and simply not ready for prime time. For many end-users, there has clearly been more smoke than fire, with a field of several dozen vendors — some of whom have departed due to acquisition or failure.
The field of video analytics — otherwise known as video content analysis — finds its roots in the broader field of computer vision, and, more specifically, machine vision, which evolved in the late 1970s and was used extensively in automotive manufacturing and other industries for parts inspection and quality control. The technology has also been used successfully in the Intelligent Transportation Systems (ITS) market for control of traffic lights at intersections and license plate recognition (LPR).
It only seems natural, then, that the technology would be applied to CCTV in physical security — where, when supplemented with some rules of logic, the value of surveillance cameras could be drastically enhanced, both for real-time and for forensic analysis. In the last 15 years, companies began to emerge to address the opportunity, with many attracting venture capital due to the technology’s obvious promise. However, the dramatic spike in interest and start-up activity that occurred early on was followed by a measurable drop in interest, as many products were plagued by underperformance, false alarms and misguided pricing policies.
But it would be a gross injustice to say that the technology does not work or that all analytic vendors are incapable. There are many cases where the technology has been successfully deployed, and, in general, these have tended to be where vendor focus has been on specific applications or environments. A la carte menus of video analytic algorithms tend not to be optimized across the board. Two areas where some success has been demonstrated are in customer oriented situations (retail, for example) and in outdoor environments. In the latter, the challenges of lighting, heat, wind, vibration and background clutter are quite difficult, and vendors have been forced to focus on those specific challenges to be successful.
It is also important to note that, while working technology is always nice, the business or financial value to the customer must be made clear and then realized. This can be the result of reduced manpower costs, fewer cameras, decreased shrink, etc.
There are some recent technological advances that are contributing to the start of the long-term emergence of video analytics. Certainly one of the most significant is camera-level analytics, allowing a significant amount of processing to be performed at the edge. This has the effect of conserving network bandwidth by controlling times and rates of transmission as events occur, particularly helpful if transmission is via bandwidth-limited wireless networks.
Many of the embedded analytic algorithms can compensate for various camera issues and quality levels, making possible the use of lower-cost cameras or achieving better range and resolution. Closely associated with this is the increasing capability of thermal cameras (see the Jan. 2011 Tech Trends, “Thermal Imaging a Hot Technology”) in the outdoor environment leading to improved detection capability at night. Megapixel cameras represent a different challenge, as they provide a richness of scene detail and information that allows greater analysis and event discrimination to occur over larger coverage areas.
We are also seeing a trend towards tighter integration with adjacent systems including Video Management (VMS), Physical Security Information Management (PSIM), Point of Sale (POS) and Building Automation — mirroring the broader integration and interoperability trend in our industry.