The evolution of video surveillance has had a great impact on video analytics. In the transition from analog to IP, and then from low to high resolution, data has grown at an overwhelming rate. This in turn has created the need for video analytics to be able to most effectively use and analyze all this unfiltered information. As video standards continue to become more robust, the need for more intelligent analytics also increases.
One of the key issues has become how to use video analytics not just as an effective, forensic tool to analyze relevant data related to a crime that has already taken place, but as a prevention tool as well. Truly intelligent video analytics involves a decision and reaction support system that uses and analyzes data from various processes, applications, sensors and imaging devices. In a bottom line way, this means that a criminal or a terrorist may leave certain clues before an incident, such as a car parked too long in a prohibited area, unattended objects or stolen license plates. Systems that relay information to authorities in real-time can possibly help stop a crime in progress or even before it takes place.
However, as the demand for video analytics increases, so too do questions about specific applications and how best to implement this technology. One of the recurring questions is: 'where is the best place for video analytics: at the edge in the camera, or as software embedded in the servers?' There are also questions about what qualifies as truly intelligent video analytics and open architecture and what makes the most sense in the adoption process-to purchase a system with analytics built in, or to have it added later on as the need arises.
At the edge and embedded in the system
In regards to whether the system should have the analytics at the edge or embedded, integrators need to look at the purpose of the application. For instance, in a situation in which the user needs video analytics to know if a person has trespassed onto the rooftop of a high-security building, it will generally suffice to have a system that can distinguish between a human form and something else, like a bird. If the only need is to know whether it's a person or a bird (or some other kind of animal) on the roof, then having the analytics built in at the edge, right into the cameras, will suffice because there is not a need to cross reference a database to make any sort of identification.
However, extending this same situation a bit, what if security spots a person in the parking garage at an unusually late hour and wants to try to identify the individual via facial recognition? To do so, security personnel may have to cross reference various databases, in which case having a system where the analytics are built into the software of the system will be most beneficial. Other situations where that also makes sense involve container recognition, traffic monitoring and license plate recognition (LPR). In all these applications, the need to share and cross reference information is crucial, so that merely having the analytics at the edge is not sufficient.
The adoption process
There are often questions as well about the adoption process related to video analytics. In some cases, a user may not have a pressing need for video analytics and simply deals with their immediate security requirements, with the idea that down the line if they need analytics they can always bring in another vendor who specializes in that. The problem that many end-users have found out from hard experience is that analytics is not a simple add on and that dealing with a different vendor for that can be hugely disruptive to their whole video surveillance system.
Security professionals are all too familiar with the sort of Murphy's Law that comes into play when adding new software programs to a system; if something can go wrong, it does often enough. As a point of comparison, changing to a new video management system is as challenging in its own way as changing one's whole system from Windows to Mac or Linux. The learning curve for the operators and administrators, including intensive, costly trainings and system downtime can quickly become overwhelming. The decision for the video management and analytics platform has to be carefully evaluated. Users should consider systems that utilize intelligence at the edge but also have the built-in capabilities of all other analytics. That way, modules can be switched on as they are needed while the staff has the convenience of the same well-known management platform.
True intelligence and true open architecture
Regarding the issue of intelligent video analytics, integrators need to look at some key criteria, namely that a truly intelligent system will be able to analyze an event in-depth and make or propose a decision regarding that. For instance, the ability to detect motion does not by itself make a system truly intelligent. As with our earlier example, the ability to distinguish between a human form and a bird, or to send an alert if a car is broken down on the highway, or to spot a package that has been left on a subway platform and notify relevant personnel, are characteristic of a system with real intelligence. To that extent, end-users need to question security professionals about the criteria they use to define truly intelligent video analytics.
In regards to architecture, it is not enough that a system provides video analytics, but interfaces need to be in place so that it communicates with other security applications like fire and burglar alarms and access control. This makes it possible to cross-verify information or provide video information for each system where an alert has been triggered. For example, in the event that a back door alarm sounds, security personnel should be able to not only view who triggered the alarm but know if it is someone who has a history of such activity. To that end, the alarm must interface with a facial recognition or face capture system that can provide the information. An access control system that employs card swipe should have that same capability. If, for instance, an employee is swiping in after hours, the system should be able to provide video immediately so that the system can verify that it is the employee in question using the card (and not someone else or perhaps a case of forced entry). Such interfaces ensure making optimal use of video analytics in relation to the whole security system.
Both the public and private sector have an increasing need for video analytics solutions. This includes banks, retail, property and building management firms, government agencies, law enforcement and counterterrorism units. However, it is only with the smart combination of all involved security processes, including non-video related information sources (such as access control and asset management) that the user can reap optimal benefits. Video analytics supports the decision process, but only the cross verification of events with other devices and databases creates a truly smart system.
For cash-strapped law enforcement agencies, such a system can reduce the need for more personnel as well as be an important crime fighting partner and an aid in traffic safety and generating revenue by spotting more offenders (whether it be running red lights, illegal turns, etc.). Counterterrorism units simply demand the latest technical innovations available, including video analytics, to more effectively spot and reduce potential threats. As the role of video analytics increases, security sales forces and integration specialists must be knowledgeable experts prepared to custom fit solutions for their customers. This is one area in which one size definitely does not fit all.
