Artificial intelligence for video surveillance

Security contractors program the software to define restricted areas within the camera's view (such as a fenced off area, a parking lot but not the sidewalk or public street outside the lot) and program for times of day (such as after the close of business) for the property being protected by the camera surveillance.

sends an alert if it detects a trespasser breaking the "rule" set that no person is allowed in that area during that time of day.

learns what is normal behaviour for people, vehicles, machines, and the environment based on its own observation of patterns of various characteristics such as size, speed, reflectivity, color, grouping, vertical or horizontal orientation and so forth.

Limitations in the ability of humans to vigilantly monitor video surveillance live footage led to the demand for artificial intelligence that could better serve the task.

Humans watching a single video monitor for more than twenty minutes lose 95% of their ability to maintain attention sufficient to discern significant events.

In general, the camera views of empty hallways, storage facilities, parking lots or structures are exceedingly boring and thus attention quickly diminishes.

While video surveillance cameras proliferated with great adoption by users ranging from car dealerships and shopping plazas to schools and businesses to highly secured facilities such as nuclear plants, it was recognized in hindsight that video surveillance by human officers (also called "operators") was impractical and ineffective.

Extensive video surveillance systems were relegated to merely recording for possible forensic use to identify someone, after the fact of a theft, arson, attack or incident.

Where wide angle camera views were employed, particularly for large outdoor areas, severe limitations were discovered even for this purpose due to insufficient resolution.

[citation needed] In response to the shortcomings of human guards to watch surveillance monitors long-term, the first solution was to add motion detectors to cameras.

It was reasoned that an intruder's or perpetrator's motion would send an alert to the remote monitoring officer obviating the need for constant human vigilance.

This caused hundreds or even thousands of false alerts per day, rendering this solution inoperable except in indoor environments during times of non-operating hours.

When the object of interest, for example a human, violates a preset rule, for example that the number of people shall not exceed zero in a pre-defined area during a defined time interval, then an alert is sent.

A red rectangle or so-called "bounding box" will typically automatically follow the detected intruder, and a short video clip of this is sent as the alert.

At night, even in illuminated outdoor areas, a moving subject does not gather enough light per frame per second and so, unless quite close to the camera, will appear as a thin wisp or barely discernible ghost or completely invisible.

Using statistical models of degrees of deviation from its learned pattern of what constitutes the human form it will detect an intruder with high reliability and a low false alert rate even in adverse conditions.

A one megapixel camera with the onboard video analytics was able to detect a human at a distance of about 350' and an angle of view of about 30 degrees in non-ideal conditions.

One of the most powerful features of the system is that a human officer or operator, receiving an alert from the A.I., could immediately talk down over outdoor public address loudspeakers to the intruder.

This had high deterrence value as most crimes are opportunistic and the risk of capture to the intruder becomes so pronounced when a live person is talking to them that they are very likely to desist from intrusion and to retreat.

Accordingly, the police give very low priority response to burglar alarms and can take from twenty minutes to two hours to respond to the site.

It is simply finding characteristics of these things based on their size, shape, color, reflectivity, angle, orientation, motion, and so on.

Motion detecting cameras miss some true positives but are plagued with overwhelming false alarms in an outdoor environment.

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.

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 utility of artificial intelligence for security does not exist in a vacuum, and its development was not driven by purely academic or scientific study.

to compile and recognize patterns consisting of very large data sets requiring simultaneous calculations in multiple remote viewed locations.

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.

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.