Computer vision is a technology that gives machines the ability to perceive the world around us similarly to humans. At present, computer vision solutions chiefly rely on artificial intelligence and deep learning to analyze visual information, including images and videos, and make decisions based on such analysis.

Quick history of computer vision

In the 1950s and 1960s, computer vision was mainly concerned with extracting the edges of objects in an image. In the next decade, the use of mathematical analysis led to the first commercial application of the technology in a text-to-speech converter.

Throughout the 1990s, the development of the internet provided a growing image base for computer vision research. In 2001 two MIT scientists presented an algorithm capable of spotting human faces in images in real time.

In the XXI century computer vision became a popular automation tool for image processing. Classification and image recognition solutions were implemented across multiple industries.

The multiple commercial benefits of computer vision are oft-discussed in professional circles. But what applications of computer vision are readily accessible to a regular person?

Applications of computer vision in the modern lifestyle

Driving

Approximately 1.35 million lives are lost in road crashes each year. Various efforts to make driving safer now include the research into self-driving vehicles, a technology that hinges on real-time image analysis. There are still many challenges to overcome, technology-wise as well as legislation-wise. In some cities, passengers can already enjoy self-driving taxis and public transport. Self-driving mode in personal cars is also becoming more common.

Driverless trucks technology

Starsky Robotics is a startup focused on developing fully autonomous freight trucks, the solution that can help tackle “one of the most dangerous jobs in America” and make the roads safer for everyone.

Personal finance management

Many banking apps now leverage face recognition for biometric authentication and authorization, which is faster and more reliable than regular passwords: all you need to access your funds is a smartphone camera.

Another trend in banking is AI-enabled face recognition in ATMs that verifies the identities of card holders and makes it impossible to withdraw cash from a stolen card.

Computer vision apps are used as personal financial consultants to keep spending data from receipts, convert currencies on the go, or pay paper bills online in a few taps.

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Tourism

Sometimes there’s no one around to answer your questions or show you the way, or you just don’t speak the language. A lot of apps for tourists now use computer vision technology to power innovative features that help to resolve such difficulties so that nothing can dampen the enjoyment from your trip.

Google’s instant translation

Google’s instant translation

Google Translate app, for example, now has the instant camera feature capable of detecting languages from text when you point your smartphone at, say, a road sign. The detected text is translated into your native language right then and there.

Image recognition is also a popular feature in modern museum guide apps, providing visitors with background information about the artworks and helping them navigate the grounds.

Sports and fitness

True sports fans always want to learn more about their favorite teams or athletes. By having all the facts at their fingertips, they can judge the competition as it unfolds.

Mobile apps developers leverage computer vision to enable real-time tracking of players and sports equipment, enrich viewing experiences with additional insights, and enhance loyalty programs.

Live action stats

Live action stats

A new app for basketball fans harnesses computer vision and statistical analysis technology to create the ultimate mobile engagement tool. All the fan has to do is point their smartphone camera at the court and tap any player to view performance stats. Under the hood, the solution uses real-time data from ball- and player-tracking sensors to recognize athletes and monitor their movements.

Shopping

It’s impossible to talk about the advances of computer vision in retail without mentioning Amazon Go, the first store with an entirely virtual checkout (the ‘just walk out’ technology). Amazon Go uses machine vision to identify every item a customer is taking and track the items that have been put back on the shelf. The combination of powerful algorithms with high-quality video streaming enables the system to generate a precise shopping list for every customer and charge their cards correctly.

Digital fitting rooms equipped with cameras can recognize the pieces of clothing the customer brought in, suggest the best matching items in different styles and colors, or even let customers try on makeup and accessories using special mirror overlays.

Media consumption

Computer vision is applied in the media and entertainment industry to streamline content production, delivery, and monetization. A regular viewer sees the results of this work: high-quality content made accessible through meticulously organized, interactive applications that provide hyper-personalized recommendations on top of it all.

Parents are able to activate robust viewing filters that rely on computer vision to correctly flag offensive content and keep it away from children. There’s even an app that allows you to identify and buy outfits your favorite characters wear just by taking a screenshot!

Healthcare

Computer vision applications for medical imaging can be found in almost every hospital these days. Chances are that your CT scan, X-ray, or MRI has been processed by AI to help the doctor make an accurate assessment. Computer vision solutions assist clinicians in cancer screening, surgical manipulations, blood testing, dentistry, physical rehabilitation, and many other treatments.

Besides high-end medical solutions, there are simpler ways to use computer vision to keep ourselves healthy, such as a mobile app that identifies pills and helps people keep up with their prescriptions. Another recent example is an app that can detect tonsillitis using your smartphone camera.

The digital fitness industry is also beginning to adopt computer vision to forge a stronger connection with users. For example, Tempo, a home gym solution, is equipped with a 3D motion capture system and an artificial intelligence core to help athletes perfect their techniques and keep the workouts safe.

Workplace safety

Manufacturing jobs worldwide have some of the highest rates of workplace injury, so every industry is looking for effective solutions to workplace safety issues. In this context, computer vision can be applied to automate certain tasks such as equipment inspection and maintenance, making them remote and less dangerous for workers.

Computer vision applications are also implemented on the shopfloor where human involvement is inevitable. When a worker gets too close to a dangerous mechanism or enters a hazardous zone, the system can issue a warning or automatically shut down the mechanism.

Learning

In the education and training sector, computer vision brings innovation to remote learning solutions by automating teacher efficiency assessment, student engagement evaluation, and homework grading. Such human-focused subsets of CV as face recognition, eye-tracking, posture detection, and emotion recognition are particularly valuable in this regard.

Face recognition and face tracking also make part of online proctoring platforms where they serve to verify the identity of test-takers and ensure the integrity of exams in a non-obstructive way.

Growing food

Agriculture was one of the last industries to start integrating computer vision technologies, but a slow start was followed by a boom of inventions. From semi-autonomous harvesters that use computer vision to analyze grain and adjust their routes, to drone-enabled pesticide spraying, to livestock tracking, the potential of computer vision in agriculture has become evident.

Computer vision benefits in agriculture

You don’t need a fleet of agricultural machinery to try agricultural computer vision. There are now solutions available even to private farmers that provide access to a database of high-resolution satellite images and use sophisticated computer vision algorithms to unlock the data in them. Users can monitor their fields, get relevant weather reports, determine the necessary amount of fertilizer, and predict issues before they arise.

Conclusion

The adoption of computer vision across industries will only accelerate as the pandemic amplifies the demand for solutions that can replace human oversight.

With big players pushing the envelope, the computer vision technology will become more accessible to regular consumers as well. We can expect more day-to-day tasks taken off our hands and more services moving online, from tutoring to personalized fitness to medical consultations.

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