Learning to Adapt

Video analytics plays a key role in revolutionizing physical security

A new type of video surveillance technology called adaptive learning video analytics is revolutionizing physical security with its ability to analyze input from hundreds or even thousands of security cameras and provide alerts to potential threats as they occur. Conventional video surveillance systems are often ineffective, since they rely on people to monitor all the cameras’ output, which is a virtual impossibility. Some solutions use rules-based algorithms to analyze video output and detect one specific behavior—but human behavior is too various for this approach to be effective. And neither approach provides alerts in real time.

Adaptive learning video analytics takes a different approach by analyzing the output of video cameras in real time to detect—and alert on—abnormal behavior. Because this technology is computerbased, it brings physical security into the realm of IT like never before. In fact, IT will play an important role in implementing and managing this next generation of video surveillance systems and thus will need to learn about these systems and how they work.

Intelligent Technology
The basic concept of this new technology relates to other, wellunderstood technologies that IT departments already use, such as software-based performance management and network security products that employ pattern recognition. These products analyze a system’s performance over time to learn normal patterns of activity and recognize activity patterns that are abnormal, without human input. They also send alarms that alert users to any detected abnormal activity.

A system’s performance management product might learn, for example, that there is normally a spike of e-mail activity at 8 a.m., as people start their day’s work, but that a spike of email activity at 4 a.m. is not normal. This abnormal activity, which could represent an attack by a worm or virus, will trigger an alert.

The Core of Surveillance Technology
Adaptive learning video analytics uses similar principles. However, it has extra layers of complexity because its roots are not only in intelligent pattern recognition but also in observations made by video analytic algorithms. Video surveillance systems are designed to minimize threats from people, but people’s behavior is unpredictable. It is impossible to write a set of rules that can be expected to cover the full range of possible behaviors for any given environment. Instead, the system must be able to learn what is normal and what isn’t.

In essence, adaptive learning video analytics follows the human cognitive model for processing visual inputs into knowledge and requires a combination of computer vision, video analytics and machine-learning capabilities. The technology takes the input from existing video security cameras— the eyes of the system—recognizes and identifies the objects in each frame to learn what activity normally takes place within the area under surveillance; analyzes the changes, activities and motions of those objects; and builds a model of established behaviors.

Finally, adaptive learning video analytics provides a wide range of alert systems that can raise awareness and report abnormal or high-risk behaviors. This is possible because the system can compare current behaviors to patterns it has learned through observation. All these activities take place automatically, without the need of constant human involvement to create rules and update settings whenever a camera is deployed to a new location.

Unlike programmed rules-based solutions, adaptive learning video analytics can continuously improve on its accuracy and utility by adapting to changes in the observed environment, thereby also improving the value of the technology. This constant self-calibration and self-improvement enables the technology to continuously provide accurate analysis of potentially threatening behaviors.

Physical, IT Security Strategies
Adaptive learning video analytics accelerates the convergence of strategies for physical security and IT security. The technology is computationally intensive and thus has hardware and networking requirements that fall into the domain of IT.

Computing environment. The new adaptive learning video analytics technology is run on a series of servers that need to be deployed in a data center environment, complete with appropriate power and cooling requirements. These systems also must maximize CPU and memory utilization, and they are best suited to running on lean operating systems such as Linux.

Protocols. When evaluating video surveillance systems, it is important to remember that many employ proprietary protocols to perform functions such as moving video streams and are therefore often not compatible with existing IT infrastructure. Adaptive learning video analytics embraces open standards, employing the real-time streaming protocol for communication of video data and other standard protocols such as lightweight directory access protocol, which makes it much easier for IT to add the infrastructure required to support video surveillance.

Use of other Web service protocols such as extensible markup language and simple object access protocol also means the technology can integrate with existing IT networks to correlate all types of security alerts.

Integration with IT. Because video is bandwidth intensive, IT must be able to provide sufficient bandwidth between the servers handling adaptive learning video analytics and other enterprise servers. It also is important to adhere to industry standards regarding data compatibility and to be up-to-date on how they are evolving. Video compression standards have improved greatly in recent years, so it is always best to insist upon modern video compression encodings such as MPEG-4, H.263 and H.264. These protocols will save significant bandwidth when building a large deployment of cameras integrated with adaptive learning video analytics.

Architecture. An effective video analytics system must be able to communicate and interoperate with business logic and data, so it is important to employ a Web services platform architecture.

As more physical security solutions continue to incorporate intelligent software to augment the capabilities of their operations, it will become natural for these solutions to migrate under the domain of IT. In addition, as physical security continues to evolve from “guns, gates and guards” to using a wide variety of intelligent technologies, it also will eventually be migrated into and managed as an additional component of an organization’s overall corporate IT security model.

The sooner IT begins learning about how these technologies integrate into its domain, the sooner these technologies can be leveraged to benefit the enterprise as a whole.

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