Computer vision (sometimes called machine vision) is one of the most exciting applications of artificial intelligence. Here we look at the five biggest trends in this fast-developing area.

1. THE 5 BIGGEST COMPUTER VISION
TRENDS IN 2022

2. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
THE 5 BIGGEST COMPUTER VISION TRENDS IN
2022
Computer vision (sometimes called machine vision) is one of the most exciting
applications of artificial intelligence. Here we look at the five biggest trends in
this fast-developing area.

3. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
Computer vision (sometimes called machine vision) is one of the most exciting applications
of artificial intelligence. Algorithms that are able to understand images – both pictures and
moving video – are a key technological foundation behind many innovations, from
autonomous, self-driving vehicles to smart industrial machinery and even the filters on your
phone that make the pictures you upload to Instagram look more pretty.
Along with language processing abilities (natural language processing, or “NLP”) its
fundamental to our efforts to build machines that are capable of understanding and
learning about the world around them, just like we do. Generally, it involves applications
powered by deep learning – neural networks trained on thousands, millions or billions of
images until they become experts at classifying what they can "see."

4. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
The value of the market in computer vision technology is predicted to hit $48
billion by the end of 2022 and is likely to be a source of ongoing innovation and
breakthroughs throughout the year. So let’s take a look at some of the key trends
we’ll be following involving this fascinating technology:

5. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
DATA-CENTRIC COMPUTER VISION
Data-centric artificial intelligence is based on the idea that equal, if not more, focus should be
put into optimizing the quality of data used to train algorithms, as is put into developing the
models and algorithms themselves. Championed by Andrew Ng – renowned pioneer of deep
learning – this newly emerging paradigm is relevant across AI disciplines but particularly so in
the field of computer vision. Some of the first deep learning-based based image recognition
models were developed by Dr. Ng at Google, with the purpose of training computers to
recognize pictures of cats, and they are particularly dependent on the quality of the data they
are fed, rather than just the quantity. This focus on iteratively improving the quality of labeling –
using automated techniques of extracting and labeling data – will allow computer vision
technology to be applied to problems where far less data is available, potentially lowering the
cost (in terms of money as well as compute resources) and opening up many new potential use
cases.

6. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
COMPUTER VISION IN HEALTH AND SAFETY
A key use case for computer vision is spotting dangers and raising alarms when something is going
wrong. Methods have been developed for allowing computers to detect unsafe behavior on
construction sites – such as workers without hard hats or safety harnesses, as well as monitor
environments where heavy machinery such as forklift trucks are working in proximity to humans,
enabling them to be automatically shut down if someone steps into their path. With 2.7 million
injuries caused due to workplace accidents every year, according to the US Bureau of Labor
Statistics, this is an area where businesses are increasingly investing in order to reduce the human
and financial costs caused by oversight or inattentiveness. Of course, preventing the spread of
illness caused by viruses is also an important use case these days, and here computer vision
technologies are increasingly being deployed to monitor compliance with social distancing
requirements, as well as mask-wearing mandates. Computer vision algorithms have also been
developed during the current pandemic in order to assist with diagnosing infection from chest x-
rays by looking for evidence of infection and damage to images of lungs.

7. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
COMPUTER VISION IN RETAIL
Shopping and retail are other aspects of life where we are sure to notice the
increasing prevalence of computer vision technology during 2022. Amazon has
pioneered the concept of cashier-less stores with its Go grocery stores,
equipped with cameras that simply recognize which items customers are taking
from the shelves. More branches will open throughout 2022, and other retailers
will jump on the bandwagon, including Tesco, which will open the UK’s first
checkout-free supermarket.

8. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
As well as relieving humans of the responsibility of scanning purchases, computer vision has a
number of other uses in retail, including inventory management, where cameras are used to
check stock levels on shelves and in warehouses and automatically order replenishment when
necessary. It's also been used to monitor and understand the movement patterns of customers
around stores in order to optimize the positioning of goods and, of course, in security systems
to deter shoplifters. Another increasingly popular use case involves allowing customers to get
information on products by scanning barcodes using their mobile phones. In fashion retail, one
particularly fun application of computer vision is the “virtual fitting room," which allows
shoppers to virtually try on items without touching them – cameras in the mirror simply
superimpose images of the clothing on the mirror’s reflection, and can even identify products
customers are trying on and suggest matching accessories to go with them.

9. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
COMPUTER VISION IN CONNECTED AND AUTONOMOUS CARS
Computer vision is an integral element of the connected systems in modern cars. Although
our first thoughts might be of the upcoming autonomous vehicles, it has a number of other
uses in the existing range of “connected” cars that are already on the roads and parked in our
garages. Systems have been developed that use cameras to track facial expressions to look
for warning signs that we may be getting tired and risking falling asleep at the wheel. As this
is said to be a factor in up to 25% of fatal and serious road accidents, it’s clear to see that
measures like this could easily save lives. This technology is already in use in commercial
vehicles such as freight trucks, and in 2022 we could see it start to make its way into personal
cars too. Other proposed uses for computer vision in cars that could make it from drawing
board to reality include monitoring whether seatbelts are being worn and even whether
passengers are leaving keys and phones behind as they leave taxis and ride-sharing vehicles.

10. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
Of course, computer vision will play a big part in self-driving, too – current
thinking is that it will be the most important on-board element of autonomous
navigation. Tesla announced this year that its cars will rely primarily on
computer vision rather than lidar and radar, which use laser and radio waves,
respectively, to build a model of the car’s environment.

11. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
COMPUTER VISION AT THE EDGE
Edge computing describes systems where computation is carried out as close as possible to the data
source. It’s a term that’s used in contrast to the paradigm of cloud computing, where data is collected
via sensors and sent to centralized servers for storage and processing. In the domain of computer
vision, it is an increasingly useful concept, as computer vision systems often do jobs where action
needs to be taken immediately (think of the use cases mentioned in this article under safety and
autonomous driving), and there simply isn't time for data to be sent to the cloud!
As well as the increases in speed that can be achieved, edge computing in relation to computer vision
has important implications for security – an important factor to consider as businesses and individuals
face tighter scrutiny and regulation over the way video data is captured and used. With edge devices
such as computer vision-equipped security cameras, data can be analyzed on the fly and discarded if
there is no reason for it to be kept, for example, if no suspicious activity is detected.

12. © 2021 Bernard Marr, Bernard Marr & Co. All rights reserved
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out my books ‘Business Trends in Practice’, ‘Extended Reality in Practice' and
‘Tech Trends in Practice.'

13. © 2021 Bernard Marr , Bernard Marr & Co. All rights reserved
Bernard Marr is an internationally best-selling author, popular keynote speaker,
futurist, and a strategic business & technology advisor to governments and
companies. He helps organisations improve their business performance, use data
more intelligently, and understand the implications of new technologies such as
artificial intelligence, big data, blockchains, and the Internet of Things.
LinkedIn has ranked Bernard as one of the world’s top 5 business influencers. He is
a frequent contributor to the World Economic Forum and writes a regular column for
Forbes. Every day Bernard actively engages his 1.5 million social media followers
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