Unassisted AI Video Surveillance Techniques Help Numerous Verticals to Scale.
Unassisted AI Video Surveillance Techniques Help Numerous Verticals to Scale. Data has always been a business game changer as a rear view indicator. It’s been defined as the new oil. Most of the time, data gets collected, stored, and then analyzed to find the right insights through multiple sets of tools. With this cumbersome approach, reaching those critical data points requires considerable time. In the process, opportunities are lost and greater costs are accumulated.
Artificial intelligence for video surveillance utilizes computer software programs that analyze the images from video surveillance cameras in order to recognize humans, vehicles or objects. 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 behavior 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. normalizes 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 behavior for the various observed objects. After several weeks of learning in this fashion it can recognize 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.