The most powerful and compelling types of AI is computer vision. It focuses on replicating parts of the complexity of the human vision system and enabling computers to identify and process objects in images and videos in the same way that humans do.
As a result of advancement in artificial intelligence and innovations in deep learning & neural networks, the field has been able to take great leaps in recent years and has been able to surpass humans in some tasks related to detecting and labelling objects.
One of the driving factors behind the growth of computer vision are the amount of data we generate today and the computing power that makes computer vision better. As the field of computer vision has grown with new hardware and algorithms so has the accuracy rates for object identification. In less than a decade, today’s systems have reached 99 percent accuracy from 50 percent making them more accurate than humans at quickly reacting to visual inputs.
On a certain level Computer vision is all about pattern recognition. So one way to train a computer how to understand visual data is to feed it images, lots of images thousands, millions if possible that have been labelledand then subject those to various software techniques, or algorithms, that allow the computer to hunt down patterns in all the elements that relate to those labels.
Computer vision is one of the areas in Machine Learning where core concepts are already being integrated into major products such as Self-Driving Cars, Facial Recognition, Augmented Reality & Mixed Reality, Healthcare.
Case Study - Customer Behaviour Tracking
Online stores like Amazon have long been able to take advantage of their digital platform’s analysis capabilities. Customer behavior can be analyzed in detail and the user experience can be optimized. The retail industry is also trying to optimize the experience of its customers and make it ideal. Until now, the tools to automatically capture the interaction of people with displayed items have been missing. Computer vision is now able to close this gap for the retail industry.
In combination with existing security cameras, algorithms can automatically evaluate video material and study customer behavior. For example, the current number of people in the store can be counted at any time, which is a useful application during the COVID-19 pandemic with its restrictions on the maximum number of visitors allowed in stores. But more interesting might be analyses on the individual level, such as the chosen route through the store and individual departments. This allows the design, structure and placement of products to be optimized, traffic jams in well-visited departments to be avoided and the customers’ overall user experience to be improved. Revolutionary is the ability to track the attention that individual shelves and products receive from customers. Specialized algorithms can detect the direction of people’s gaze and thus measure how long any given object is viewed by passers-by.
With the help of this technology, retailers now have the opportunity to catch up with online trading and to evaluate customer behavior within their stores in detail. This increases sales, minimizes the time spent in the store and optimizes the distribution of customers within the store.