The explosion in the volume of visual data, coupled with the increased sophistication of artificial neural networks and the availability of chips created specifically for artificial intelligence processes, will all drive growth in computer vision (CV) over the coming years. CV will find myriad applications in several industries, reaching an estimated market size of $28bn by 2030, according to GlobalData forecasts.

Listed below are the top technology trends in computer vision, as identified by GlobalData.

CV as a Service (CVaaS)

CVaaS is a type of software-as-a-service hosted in the cloud. It allows businesses to rent rather than build a computer vision platform. Running CV in the cloud has brought the technology to new customers. The on-demand access to algorithms and APIs under a pay-as-you-go model, make the technology both scalable and affordable.

In the years to come, CVaaS will become a key part of a business’s automation process. Also, CV models will be increasingly run at the edge, allowing them to be embedded in a greater number of devices.

Convolutional neural networks (CNNs)

CNNs are a class of deep neural networks that specialises in analysing visual images. CNNs are made up of neurons that improve through learning. They are based on multi-layer architecture that makes it easy to deal with images. This is achieved by using a special kind of mathematical operation called convolution. While CNNs were invented in the 1980s, they were only fully implemented after the introduction of the graphic processing unit (GPU). The combination of CNNs and GPUs, which give deep learning technologies a performance boost, will be one of the main drivers in CV growth.


GPUs are valued for their ability to process large blocks of data in parallel at very high speed. This is crucial for neural network training. Originally intended for graphic rendering in computer games, GPUs are designed to handle image processing quickly and efficiently. NVIDIA has dominated the GPU market for many years. Nvidia’s second generation Tensor Core GPUs are designed to accelerate both AI training and inference for computer vision. However, AMD gained significant market share and eventually overtook its rival in 2019. Another threat for Nvidia is Intel, which is expected to launch its Xe graphics card in 2020. With this new discrete graphics card, the chip giant plans to combine GPUs with its CPUs and focus on AI and ML applications.

Autonomous vehicles (AVs)

The first fully autonomous vehicles are expected to be operating in limited zones between 2020 and 2025. Therefore, the industry will be investing heavily in the development of autonomous vehicles software which uses sensor-fed ML algorithms. Companies like Alphabet’s Waymo, Tesla, Uber, Baidu, Aptiv/NuTonomy, Nvidia, Intel/Mobileye, and GM Cruise are all competing to be leading players.

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By GlobalData

Neural network-based ML will become a standard auto component, and will run on algorithm-specific AI. However, the path is rockier than predicted. In September 2019, Morgan Stanley cut its valuation of Waymo by 40%, citing delays in technology development. This, and other fatalities involving AVs have highlighted the limitations in current ML systems, which learn from existing data only and struggle with situations that they have never seen before.

Ambient commerce

CV is one of the primary technologies for ambient commerce. Using sensors and ML in physical stores, CV technology detects when an item is removed from a shelf and who took it. CV tracks customer activity throughout the store. Amazon Go uses hundreds of cameras to track customers constantly.

Emotion AI

Emotion AI uses CV technology to analyse facial expressions and eye movements in photos and videos, with the aim of reading a person’s emotional responses. A London-based emotion AI start-up records the facial expression of a sample audience who watch a certain ad through the camera of their computer or smartphone. Realeyes, raised $12.4m of funding in 2019 to help big brands, such as AT&T, Mars, Hershey’s, and Coca-Cola, detect emotion from images of facial expressions before rating each ad for attention, emotion, sentiment. Apple also acquired Emotient in 2016, and Facebook is developing its own products.

Smart cities

Surveillance technology, in the form of AI tools including CV, is increasingly deployed in smart cities to improve efficiency. However, sometimes the level of surveillance is more intrusive than needed to improve urban services. In autocratic and semi-autocratic countries, CV is a tool of mass surveillance. A 2019 report by the CEIP found that at least 75 countries around the world, including the US, Brazil, Germany, India, and Singapore are using AI tools, including CV, to monitor citizens’ activities. Such widespread surveillance violates international standards for privacy, which state that both collection and use of biometric data should be limited to people found to be involved in wrongdoing, and not from the mass population with no specific link to crime.

This is an edited extract from the Computer Vision – Thematic Research report produced by GlobalData Thematic Research.