Artificial intelligence for video surveillance utilizes computer software programs that analyze the audio and images from video surveillance cameras in order to recognize humans, vehicles, objects, attributes, and events. 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. The artificial intelligence ("A.I.") 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.

The A.I. program functions by using machine vision. Machine vision is a series of algorithms, or mathematical procedures, which work like a flow-chart or series of questions to compare the object seen with hundreds of thousands of stored reference images of humans in different postures, angles, positions and movements. The A.I. asks itself if the observed object moves like the reference images, whether it is approximately the same size height relative to width, if it has the characteristic two arms and two legs, if it moves with similar speed, and if it is vertical instead of horizontal. Many other questions are possible, such as the degree to which the object is reflective, the degree to which it is steady or vibrating, and the smoothness with which it moves. Combining all of the values from the various questions, an overall ranking is derived which gives the A.I. the probability that the object is or is not a human. If the value exceeds a limit that is set, then the alert is sent. It is characteristic of such programs that they are self-learning to a degree, learning, for example that humans or vehicles appear bigger in certain portions of the monitored image – those areas near the camera – than in other portions, those being the areas farthest from the camera.

In addition to the simple rule restricting humans or vehicles from certain areas at certain times of day, more complex rules can be set. The user of the system may wish to know if vehicles drive in one direction but not the other. Users may wish to know that there are more than a certain preset number of people within a particular area. The A.I. is capable of maintaining surveillance of hundreds of cameras simultaneously. Its ability to spot a trespasser in the distance or in rain or glare is superior to humans' ability to do so.

This type of A.I. for security is known as "rule-based" because a human programmer must set rules for all of the things for which the user wishes to be alerted. This is the most prevalent form of A.I. for security. Many video surveillance camera systems today include this type of A.I. capability. The hard-drive that houses the program can either be located in the cameras themselves or can be in a separate device that receives the input from the cameras.

A newer, non-rule based form of A.I. for security called "behavioral analytics" has been developed. This software is fully self-learning with no initial programming input by the user or security contractor. In this type of analytics, the A.I. 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. The A.I. normalises the visual data, meaning that it classifies and tags the objects and patterns it observes, building up continuously refined definitions of what is normal or average behaviour for the various observed objects. After several weeks of learning in this fashion it can recognise when things break the pattern. When it observes such anomalies it sends an alert. For example, it is normal for cars to drive in the street. A car seen driving up onto a sidewalk would be an anomaly. If a fenced yard is normally empty at night, then a person entering that area would be an anomaly.

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. With two monitors this is cut in half again. Given that many facilities have dozens or even hundreds of cameras, the task is clearly beyond human ability. In general, the camera views of empty hallways, storage facilities, parking lots or structures are exceedingly boring and thus attention quickly diminishes. When multiple cameras are monitored, typically employing a wall monitor or bank of monitors with split screen views and rotating every several seconds between one set of cameras and the next, the visual tedium is quickly overwhelming. 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. In these cases it is impossible to identify the trespasser or perpetrator because their image is too tiny on the monitor.

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. The problem was that in an outdoor environment there is constant motion or changes of pixels that comprise the total viewed image on screen. The motion of leaves on trees blowing in the wind, litter along the ground, insects, birds, dogs, shadows, headlights, sunbeams and so forth all comprise motion. 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.

The next evolution reduced false alerts to a degree but at the cost of complicated and time-consuming manual calibration. Here, changes of a target such as a person or vehicle relative to a fixed background are detected. Where the background changes seasonally or due to other changes, the reliability deteriorates over time. The economics of responding to too many false alerts again proved to be an obstacle and this solution was not sufficient.