Behavioral analytics

Active environments

While rule-based video analytics worked economically and reliably for many security applications there are many situations in which it cannot work. For an indoor or outdoor area where no one belongs during certain times of day, for example overnight, or for areas where no one belongs at any time such as a cell tower, traditional rule-based analytics are perfectly appropriate. In the example of a cell tower the rare time that a service technician may need to access the area would simply require calling in with a pass-code to put the monitoring response “on test” or inactivated for the brief time the authorized person was there.

But there are many security needs in active environments in which hundreds or thousands of people belong all over the place all the time. For example, a college campus, an active factory, a hospital or any active operating facility. It is not possible to set rules that would discriminate between legitimate people and criminals or wrong-doers.

Overcoming the problem of active environments

Using behavioral analytics, a self-learning, non-rule-based AI takes the data from video cameras and continuously classifies objects and events that it sees. For example, a person crossing a street is one classification. A group of people is another classification. A vehicle is one classification, but with continued learning a public bus would be discriminated from a small truck and that from a motorcycle. With increasing sophistication, the system recognizes patterns in human behavior. For example, it might observe that individuals pass through a controlled access door one at a time. The door opens, the person presents their proximity card or tag, the person passes through and the door closes. This pattern of activity, observed repeatedly, forms a basis for what is normal in the view of the camera observing that scene. Now if an authorized person opens the door but a second “tail-gating” unauthorized person grabs the door before it closes and passes through, that is the sort of anomaly that would create an alert. This type of analysis is much more complex than the rule-based analytics. While the rule-based analytics work mainly to detect intruders into areas where no one is normally present at defined times of day, the behavioral analytics works where people are active to detect things that are out of the ordinary.

A fire breaking out outdoors would be an unusual event and would cause an alert, as would a rising cloud of smoke. Vehicles driving the wrong way into a one-way driveway would also typify the type of event that has a strong visual signature and would deviate from the repeatedly observed pattern of vehicles driving the correct one-way in the lane. Someone thrown to the ground by an attacker would be an unusual event that would likely cause an alert. This is situation-specific. So if the camera viewed a gymnasium where wrestling was practiced the AI would learn it is usual for one human to throw another to the ground, in which case it would not alert on this observation.

What the artificial intelligence ‘understands’

The AI does not know or understand what a human is, or a fire, or a vehicle. It is simply finding characteristics of these things based on their size, shape, color, reflectivity, angle, orientation, motion, and so on. It then finds that the objects it has classified have typical patterns of behavior. For example, humans walk on sidewalks and sometimes on streets but they don’t climb up the sides of buildings very often. Vehicles drive on streets but don’t drive on sidewalks. Thus the anomalous behavior of someone scaling a building or a vehicle veering onto a sidewalk would trigger an alert.

Varies from traditional mindset of security systems

Typical alarm systems are designed to not miss true positives (real crime events) and to have as low of a false alarm rate as possible. In that regard, burglar alarms miss very few true positives but have a very high false alarm rate even in the controlled indoor environment. Motion detecting cameras miss some true positives but are plagued with overwhelming false alarms in an outdoor environment. Rule-based analytics reliably detect most true positives and have a low rate of false positives but cannot perform in active environments, only in empty ones. Also they are limited to the simple discrimination of whether an intruder is present or not.

Something as complex or subtle as a fight breaking out or an employee breaking a safety procedure is not possible for a rule based analytics to detect or discriminate. With behavioral analytics, it is. Places where people are moving and working do not present a problem. However, the AI may spot many things that appear anomalous but are innocent in nature. For example, if students at a campus walk on a plaza, that will be learned as normal. If a couple of students decided to carry a large sheet outdoors flapping in the wind, that might indeed trigger an alert. The monitoring officer would be alerted to look at his or her monitor and would see that the event is not a threat and would then ignore it. The degree of deviation from norm that triggers an alert can be set so that only the most abnormal things are reported. However, this still constitutes a new way of human and AI interaction not typified by the traditional alarm industry mindset. This is because there will be many false alarms that may nevertheless be valuable to send to a human officer who can quickly look and determine if the scene requires a response. In this sense, it is a “tap on the shoulder” from the AI to have the human look at something.

Limitations of behavioral analytics

Because so many complex things are being processed continuously, the software samples down to the very low resolution of only 1 CIF to conserve computational demand. The 1 CIF resolution means that an object the size of a human will not be detected if the camera utilized is wide angle and the human is more than sixty to eighty feet distant depending on conditions. Larger objects like vehicles or smoke would be detectable at greater distances.

Quantification of situational awareness

The utility of artificial intelligence for security does not exist in a vacuum, and its development was not driven by purely academic or scientific study. Rather, it is addressed to real world needs, and hence, economic forces. Its use for non-security applications such as operational efficiency, shopper heat-mapping of display areas (meaning how many people are in a certain area in a retail space), and attendance at classes are developing uses. Humans are not as well qualified as AI to compile and recognize patterns consisting of very large data sets requiring simultaneous calculations in multiple remote viewed locations. There is nothing natively human about such awareness. Such multi-tasking has been shown to defocus human attention and performance. AIs have the ability to handle such data. For the purposes of security interacting with video cameras they functionally have better visual acuity than humans or the machine approximation to it. For judging subtleties of behaviors or intentions of subjects or degrees of threat, humans remain far superior at the present state of the technology. So the AI in security functions to broadly scan beyond human capability and to vet the data to a first level of sorting of relevance and to alert the human officer who then takes over the function of assessment and response.

Security in the practical world is economically determined so that the expenditure of preventative security will never typically exceed the perceived cost of the risk to be avoided. Studies have shown that companies typically only spend about one twenty-fifth the amount on security that their actual losses cost them. What by pure economic theory should be an equivalence or homeostasis, thus falls vastly short of it. Nevertheless, security is a major expenditure, and comparison of the costs of different means of security is always foremost amongst security professionals.

Another reason that future security threats or losses are under-assessed is that often only the direct cost of a potential loss is considered instead of the spectrum of consequential losses that are concomitantly experienced.

For example, the vandalism-destruction of a custom production machine in a factory or of a refrigerated tractor trailer would result in a long replacement time during which customers could not be served, resulting in loss of their business. A violent crime will have extensive public relations damage for an employer, beyond the direct liability for failing to protect the employee.

Behavioral analytics uniquely functions beyond simple security and, due to its ability to observe breaches in standard patterns of protocols, it can effectively find unsafe acts of employees that may result in workers comp or public liability incidents. The potential of AI in the form of behavioral analytics to proactively intercept and prevent such incidents is significant.

Source: Wikipedia