Five Major Developments and Application Trends in Computer Vision Technology

Computer vision technology encompasses many algorithms that can understand images (including pictures and videos). They are the foundation of many innovative key technologies—from autonomous vehicles to intelligent industrial machinery, even software on mobile phones—and are the basis for the machines we are striving to build that can understand and learn from the surrounding world like humans.

By the end of 2022, the market value of computer vision technology is expected to reach $48 billion and may become a source of ongoing innovation and breakthroughs. Recently, Forbes reported on the five major developments and application trends of computer vision technology in 2022.

The rapid development of computer vision is thanks to the continuous advancement of deep learning technology.

Dr. Andrew Ng, a key pioneer in the field of deep learning, developed several image recognition models based on deep learning aimed at training computers to recognize images of cats. These models particularly rely on the quality of the data they are “fed” rather than just the quantity. The use of automated techniques to extract and label data enhances the quality of labeled data, allowing computer vision technology to achieve the same results with less data, thereby reducing costs in terms of funding and computational resources, and opening up more new potential use cases.

A key application of computer vision is to detect hazards and issue alerts when problems arise. Scientists have developed methods that enable computers to detect unsafe behaviors on construction sites, such as the lack of hard hats, as well as monitor various environments within the working range of heavy machinery like forklifts. If someone inadvertently enters the work area, the machines will automatically shut down. According to data from the U.S. Bureau of Labor Statistics, 2.7 million people are injured at work each year, and more companies are increasing their investments in this area to reduce the human and financial costs caused by negligence.

Of course, preventing the widespread transmission of viruses is also an important application case. Computer vision technology is increasingly being used to monitor whether individuals are adhering to social distancing regulations and whether they are wearing masks. During the COVID-19 pandemic, scientists also developed computer vision algorithms that could help diagnose patients by looking for evidence of infection and damage to lung images.

In 2022, computer vision technology will be widely adopted in shopping and retail.

Previously, Amazon pioneered cashier-less stores with Amazon Go, equipped with cameras that can easily recognize items customers take from the shelves. More stores are expected to open in 2022, with other retailers, including Tesco, joining in; for example, Tesco will open its first cashier-less supermarket in the UK.

In addition to automatically scanning products, computer vision has many other applications in retail, such as inventory management, where cameras can check the placement of products on shelves and stock levels in warehouses, automatically reordering supplies when necessary. It is also used to monitor and understand customer movement patterns in stores to optimize product placement, and of course, to prevent theft. Another increasingly popular use case for computer vision technology is allowing customers to scan barcodes with their mobile phones to obtain product information. In the fashion retail sector, a particularly interesting application of computer vision is the “virtual fitting room,” where customers can try on items virtually without touching them, and even identify the products they are trying on and provide matching suggestions.

Computer vision has already been applied in the field of intelligent connected vehicles. Intelligent connected vehicles refer to a new generation of cars equipped with advanced onboard sensors, controllers, and actuators, integrating modern communication and network technology to achieve intelligent information exchange and sharing between vehicles, people, roads, and back-end systems, ensuring safe, comfortable, energy-efficient, and efficient driving, ultimately capable of replacing human operation.

Scientists have developed visual systems that can track drivers’ facial expressions using cameras and issue warning signals, such as indicating that the driver may be fatigued and at risk of falling asleep while driving. Studies show that up to 25% of fatal and serious traffic accidents are caused by this factor, so such technologies and measures can better save lives.

This technology has already been used in commercial vehicles such as freight trucks, and by 2022, it is expected to enter the private car market. Other potential uses of computer vision in the automotive field include monitoring whether passengers are wearing seat belts and even checking if keys and phones are left behind when exiting the vehicle.

Of course, computer vision will also play an important role in the field of autonomous vehicles. For instance, Tesla announced this year that its cars will primarily rely on computer vision rather than using radar to model the environment around the vehicle.

Edge computing refers to providing the nearest end services using an open platform near the data source. Edge computing is the opposite of cloud computing, which refers to breaking down numerous data processing programs through the network and reprocessing and analyzing these decomposed small programs using a system composed of servers to obtain results.

In the field of computer vision, the importance of edge computing technology is increasing day by day, as computer vision systems often need to make quick decisions, such as in the field of autonomous vehicles, where there is simply no time to send data to the cloud.

As the computational speed of edge computing continues to improve, computer vision will have a significant impact on security, especially as businesses and individuals face stricter scrutiny and regulation regarding how they capture and use video data. Using edge devices, such as security cameras equipped with computer vision, people can analyze data dynamically and discard data without a reason to retain it (e.g., if no suspicious activity is detected).

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