The five considerations in advancing video surveillance in security

The use and sophistication of video surveillance systems grows, and more CSOs are finding it is necessary to integrate video into overall IT security. Eric Eaton, CTO of BRS Labs, offers a useful discussion of the top five criteria to consider when evaluating a video surveillance solution.

He writes that Video surveillance was once the exclusive province of physical security; operators looked at multiple video screens, each displaying the field of view of a single video camera, to monitor for security incidents. Increasingly, though, the charge of fully securing an organization’s assets requires a larger number of cameras with multiple viewers of the video information. These systems add more video to be watched, so there is a need to use IT style analysis tools to help sort through the myriad of incoming video to find potential threats. With that, CSOs find themselves required to integrate a key physical security solution, video surveillance into overall IT security. “While this means understanding what video surveillance can and can’t do, it also reveals the need for education on evaluation criteria for video surveillance solutions and an understanding of what else can be done to improve existing systems,” Eaton writes.

Here is a summary of the five criteria:

• Move from algorithms to learning. The advent of video analytics software now makes it possible for computers to “read” the output of video cameras and automatically send out alerts when abnormal behavior is detected. Initial systems tended to be small — 10 to 12 cameras, six-month implementation cycles, were unable to be improved or expanded once installed, and did not necessarily add much value, since they were only being programmed to alert on one type of behavior. With larger systems come challenges. It has proven challenging in video surveillance systems to program a rule for every single unusual activity, but as a natural next step, organizations will require a system that works similar to current products currently in use in IT departments to detect abnormality in network data streams. Various tools have emerged that not only “see video better,” but also analyze the digitized output of video cameras in real time to learn and recognize normal behavior, and detect and alert on all abnormal patterns of activity without any human input. Organizations may thus find the move from algorithmic and rules-based systems to a learning technology a helpful next step to improve a video surveillance system.
• Scalability Is key to success. A video analytics