Artificial intelligence can be used in the real world to:
Detect smoke and flames for early fire warning at open areas (forest, open warehouse, parking lot, etc.)
Distinguish people/vehicles from animals and other moving objects, e.g. to protect the perimeter of a nature park from poachers
Distinguish a person in a helmet and protective clothing from a person without them to prevent accidents at a dangerous production facility or construction site
Count objects of a specific type, e.g. cars in a parking lot, people in the sales floor, wares moving on a conveyor belt, etc. in non-security-related solutions
Those are just a few examples.
How behaviour analytics works
From a technical point of view, behaviour analytics combines artificial intelligence with a classic algorithmic approach. A neural network trained on a multitude of scenarios can determine the position of the bodies, heads, and limbs of humans in the camera's field of view. The algorithm outputs an array of data containing descriptions of their poses.
How behaviour analytics can be deployed
Behavioural analytics can used to ensure workplace safety. For example, tracking whether employees are holding the handrails when using the stairs at a manufacturing facility or a construction site.
What now?
Behaviour analytics can be deployed wherever your clients' imagination takes them. With this feature, practically any pose that indicates potentially dangerous behaviour can be detected. Timely response to an alarm helps avoid material damages or, in other situations, casualties.
In its most basic form, this type of analytics can be deployed to detect deviations from the search procedure in correctional facilities when a person being inspected must assume a pre-defined sequence of poses. A more advanced form allows it to detect any kind of abnormal behaviour, such as a brawl breaking out in a public space. Ideally, behaviour analytics can predict dangerous situations based on nearly imperceptible cues gleaned from collected statistics and a Big Data analysis.