Now you understand that AI is powered by machine learning and deep learning, and none of it can function without data. Imagine this scenario: you have millions of terabytes of data that are virtually useless unless someone can find a correlation between the data. This is exactly what happens in a video surveillance system that is not using machine learning or deep learning to pull meaningful information out of the data that’s being collected. Big data combined with deep learning, on the other hand, has the potential to transform video surveillance from a passive visual surveillance solution to a much more active one.
Here’s an example. Using a people-counting camera can provide valuable information on how many people are entering and exiting a defined area. It can also detect loitering, keep capacity counts, and signal an alarm when pre-defined thresholds are exceeded. Combine that with Point of Sale (POS) information, and counting how many people pass by certain retail displays can provide valuable insight. You could better determine the effectiveness of an end-cap display, such as tabulating how many people stop to look, verses those who don’t stop and compare it to actual purchases. Adding facial recognition can help detect additional patterns in browsing that would otherwise go unnoticed, such as men with beards stopping and looking at female-oriented product displays. Whether you understand why this is happening, it illuminates an interest trend nonetheless.