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By Randall R. Nason, PE, CPP
Security Technology & Design

One of the most prevalent topics among security industry practitioners today is video analytics (VA). The combination of CCTV data and rules-based algorithms hit the market hard in the 2002-2003 timeframe with the potential to solve perimeter security problems at ports and airports.

The attempt to use CCTV data as a primary security sensor is long standing and well founded — industry best practices rely heavily on a CCTV system to determine the cause of alarms, record the passage of an individual or vehicle through a controlled portal, or simply provide a record of activity in a general area of interest. The most directed use is to determine the cause of an alarm generated by any number of available intrusion sensors, ranging from a passive infrared sensor monitoring an interior area to a fence-mounted acoustic detection sensor around a Class A storage site. If properly deployed, the sensors will provide acceptably high probabilities of detection with equally acceptable nuisance alarm rates. The CCTV system is a necessary adjunct to these sensors in order to provide the necessary information for the monitoring entity initiate a proper response.

Therefore, it makes sense both from a design and implementation standpoint to somehow do away with the sensor and use the CCTV system to provide the necessary data to both detect unauthorized activity and provide the necessary data to the monitoring entity to initiate a proper response to that activity.

The allure of video analytics, however, goes far beyond just eliminating intrusion sensors. The real value is in the ability to solve once-unsolvable problems. Seaports are a clear example of sites that did not lend themselves to traditional security measures. There was no tool or technique available to provide a robust means to secure the waterside perimeter. With the heightened concern over the security of seaports, analytics offered a means to monitor a virtual perimeter, thus allowing continued waterside operations.

Industry leaders are careful to define VA as essentially different from legacy video motion detection — which simply provides an alarm based on changes in pixel grayscale levels. VA takes it one step further and attempts to determine if these pixel changes represent unauthorized activity. “Simple changes in pixels do not necessarily indicate activities of interest,” says Alan Tipton, chief technology officer for ObjectVideo. “Our software analyzes the entire video scene and creates meta data about what it sees. User-defined rules are run against the meta data to see if any activities that have been detected by the software are in violation of any rule — if so, an alert is generated.”

The Basics: The VA Process
As shown in the diagram on the previous page, the VA process can be described in three steps. First, the software continually scans the video scene looking for changes. Once a change has been detected, the second process — analyzing the changes — takes place. If the analysis determines that the changes represent a previously defined unauthorized activity, an alarm is generated.

The heart, and ultimate potential, of VA lies within this analysis process and the ability — without human intervention — to take raw video data and extract not just motion but motion that represents activity of interest based on predefined algorithmic rules. VA software and hardware manufacturers are consistent in describing their filters as examining size, shape, speed and type of movement. Simple ratios such as height-to-width are found to be surprisingly strong differentiators of people from large mammals. Another filter looks at differential motion. When a vehicle moves, all parts of the vehicle move as a unit; however, people move differently — the main portion of the body moves together, but the arms tend to move with and then against the main body movement. While a single filter cannot be relied on as definitive, multiple filters or rules can be used to increase the confidence in the classification of the video scene changes.

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