Friday, April 19, 2024

Computer Vision Market Size Gartner

Other Industries Being Disrupted By Computer Vision

Gartner Top Strategic Technology Trends for 2022

In the automotive sector, computer vision is used to assist drivers and to monitor drivers to ensure they are paying attention to the road. Its also key to enabling self-driving cars, a major growth engine for the use of computer vision in the automotive industry, says IDCs Aracaro.

But there is another key market for autonomous driving, and computer vision in general, says Arcaro: Agriculture. John Deere is doing something really critical there, he says, noting that computer vision is also being used in agriculture to sort products, to monitor plant and animal health, and to monitor and manage agricultural assets.

In cybersecurity, image analytics can be used to read signatures or spot phishing websites that are designed to look similar to real websites but different enough to evade other detection methods.

In the hospitality industry, computer vision helps track where guests go while onboard cruise ships in order to improve their experience.

In the financial services industry, image processing captures data from documents to improve efficiency of business processes.

spans almost every industry, says Dinesh Batra, vice president of data and artificial intelligence at Capgemini Invent. It has been a hugely successful tool for enterprises in recent years and its prominence will only continue to accelerate.

Artificial Intelligence In Healthcare Market

The global artificial intelligence in healthcare market size was estimated at US$ 11.06 billion in 2021 and is expected to surpass around US$ 187.95 billion by 2030, growing at a CAGR of 37% during the forecast period 2022 to 2030.

Growth Factors

The growing adoption of the digital technologies in the healthcare sector owing to the growing need for reducing the healthcare costs and offer enhanced quality patient care services to the patients are the prominent factors that are boosting the growth of the global artificial intelligence in healthcare market. The surging prevalence of various chronic diseases and growing elderly population is resulting in the increased pool of patients at hospitals. The large volume of patient health data is generated every day, which is required to be stored and managed effectively. The growing demand for the personalized medicines and the necessity of maintaining digital health records are significantly driving the artificial intelligence in healthcare market. The novel technologies like artificial intelligence and machine learning are now being integrated to the healthcare systems that will allow the health professionals in early identification of the diseases and offer enhanced care services to the patients. Moreover, the data analytics, deep learning technology, natural language processing , predictive analytics, and content analytics are supporting the healthcare professionals in early diagnosis and care services.

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Computer Vision Brings Intelligence To Retail Tech

Image Credit: JIRAROJ PRADITCHAROENKUL

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From entry to exit, the average time a consumer spends in a grocery store is about 41 minutes for one trip. But when checkout lines are long and shoppers spend time scouring shelves for out-of-stock items, that trip quickly gets much longer. Neither consumers, who may quickly lose patience nor retailers, who are already dealing with post-pandemic staffing shortages, supply chain disruptions and reduced foot traffic, want that.

That is where cashierless checkout and inventory management comes in, powered by artificial intelligence and computer vision. A variety of companies, both big tech and startups, have taken different approaches over the past few years, using cameras and sensors to identify items and ringing them up allowing the customer to quickly grab items off the shelf and leave without standing in line.

These days, even as the economy slows, investors show no signs of pulling back on investments in this sector. Big funding rounds are still making news, including the Tel Aviv-based Trigo, which last week announced a $100 million series C investment, bringing its total funding to around $199 million, according to Crunchbase.

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Streamlining And Improving Manufacturing Processes

Manufacturing is another industry being revolutionized by computer vision, which is used extensively on production lines to inspect products, automate processes, and optimize productivity.

Mike Griffin, chief data scientist at Insight, a Tempe, Ariz.-based technology consulting firm, has worked with several manufacturing clients on computer vision projects. One partnership involved developing a system in which a handheld device could be used to take a photograph of a bin of products and automatically provide a count of the number of products in the bin.

wanted to be able to hire people with disabilities to do counting, Griffin says. It sounds like an easy , but the challenge there is the vision application has to do more than interpret what it can see, but it also has to interrupt what it cant see.

Products might be stacked on top of each other, hiding those at the bottom from view. So the computer vision system had to take a two-dimensional image and translate it into a three-dimensional model. We needed to be at least 80% accurate on our inventory, including boxes wrapped in clear plastic with a lot of glare on them, says Griffin.

Well take a model thats been trained on millions of images of cats and dogs and cars and whatnot, Griffin says. So a lot of the hard work has already been done. And then we can add our 500 images of boxes or 1,000 images of tires to that model and retrain it with that additional set of images.

How Five Industries Are Using Computer Vision Technologies

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Computer vision has been around for decades, but in recent years, this technology has been advancing by leaps and bounds. Computer vision is an application of artificial intelligence that’s able to understand and interpret images, identifying and reacting with near-perfect precision due to developments in neural network technology. In fact, the accuracy of the technology has gone from 50% to 99% in less than a decade.

As computer vision becomes more advanced, its business applications grow. The global market size of computer vision was valued at $9.45 billion in 2020 and is projected to reach $41.11 billion by 2030. Computer vision has applications across a wide range of industries, but in this article, were going to zoom in on five of the most promising sectors.

1. Energy And Utility

In the energy and utility industry, computer vision is powering more efficient operations, improving safety and helping prevent harmful accidents. For example, computer vision-powered analyses of images of electric poles can detect defects in the poles that may spark and turn into fires. Thanks to predictive maintenance technologies that flag these anomalies, utility companies can then make a decision as to whether the defect needs immediate attentionand prevent events as extreme as wildfires.

2. Hospitality

3. Healthcare

4. Retail

5. Automotive

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Gartner Names Three 2022 Cool Vendors In Ai For Computer Vision

Gartners newest research highlights three 2022 Cool Vendors in AI for Computer Vision that offer innovative alternatives in the marketplace.

Analyst house Gartner, Inc. has released its newest research highlighting four emerging solution providers that data and analytics leaders should consider as compliments to their existing architectures. The 2022 Cool Vendors in Analytics and AI for Computer Vision report features information on startups that offer some disruptive capability or opportunity not common to the marketplace.

Solutions Reviews editors have read the complete report, which is available here, and want to take the opportunity to provide a brief, independent introduction to each of the cool vendors listed. Though Gartners process for selecting Cool Vendors is somewhat mysterious, we believe our unique view of the space can help you to better understand how these analytics solutions will fit into the marketplace as time goes on.

What Is Computer Vision

Computer vision is a field of artificial intelligence that is focused on processing images and videos to extract meaningful information. Examples of computer vision in action include optical character recognition, image recognition, pattern recognition, facial recognition, and object detection and classification.

Industries that make heavy use of computer vision include manufacturing, healthcare, automotive, agriculture, and logistics and supply chain. In enterprises, top drivers for deploying computer vision include automation, process improvement and productivity, and regulatory compliance and safety.

The market is growing so fast that its hard to keep tabs on it, says IDC analyst Matt Arcaro, adding that the pandemic has accelerated computer vision adoption for example, for monitoring occupancy to help ensure social distancing or to keep track of how many people were using public transit.

Because there are plenty of CCTV cameras in place, its an elegant upgrade to incorporate computer vision, Arcaro says. And, in many cases, due to government mandates or organizational choices, the investment dollars have been there.

According to IDC, the total worldwide market for computer vision technologies will grow to $2.1 billion this year, from $760 million in 2020, with a compound annual growth rate of 57% expected through 2025, to a total market value of $7.2 billion.

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Sketchar Is One Of The Top Vendors For Computer Vision Tools And Services

Sketchar is one of the top vendors of tools and services in computer vision, according to technological research and consulting firm Gartner.

5 months ago 1 min read

The annual survey, compiled by one of the leading consulting firms in the world, Gartner , included Sketchar in a list next to tech-giants like Google and Amazon.

The survey is a detailed list compiled of 102 leading IT companies with skills in computer vision tools and services. This survey is not public and can only be accessed by Gartner’s clients. If you are a client, check the list here: Tool: Vendor Identification for Computer Vision Tools and Services

We are in the world’s top 102 #computervision companies again) #sketchar

This is the second time Sketchar made it on the Gartner’s survey of the worlds leading computer vision companies.

Computer Vision Going Mainstream

Gartner Top Strategic Technology Trends for 2021

Computer vision , a field of artificial intelligence, is rapidly changing the way businesses operate. By automatically processing, analyzing, and extracting information from digital images, video files, or any visual input, CV can enable organizations to improve efficiency, reduce costs, and meet new business trends and customer demands.

From hype to reality, the rapid advancements in technologies that enable CV have moved it into mainstream commercialization. With computing power, big data, and open-source deep learning frameworks becoming more accessible to businesses of all sizes, the use of CV is no longer limited to tech giants, startups, and innovation teams.

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Vision On The Go: Saas Video Analytics Solution

The usual bottleneck of implementing an industry-wide video analytics solution is the hardware upgrade which costs a ton in a conventional surveillance system.

We are witnessing a rise in Video Analytics softwares, which can seamlessly integrate with existing infrastructure and can provide insights on the go. Softwares are being trained on over 300 different parameters for sensitive tracking with minimal false alarm rate, offer quick and easy implementation without significant hardware expenses. Softwares are customizable and easy to scale, saving both cost and time.In 2021, well see advancements in computer vision-based softwares to dynamically change any camera to AI model mapping at any time.

Computer Vision Is Primed For Business Value

Over the past few years, computer vision applications have become ubiquitous. From phones that recognize the faces of their users, to cars that drive themselves, to satellites that track ship movements, the value of computer vision has never been clear.

But hardware shortages and labor disruptions in the pandemics wake are challenging companies ability to make good on the promise of computer vision, even as the pandemic itself has accelerated the potential of its use cases.

Following is a look at how companies across a range of industries are deploying computer vision to improve and optimize key business processes, from retail fulfillment to health-care diagnostics.

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High Marks From Influencers

In Gartners Cool Vendor in AI for Computer Vision report, transportation and mobility analyst Michael Ramsey stated, AEye offers a very novel combination of sensor fusion at the processor level of camera and lidar sensors for primary use with autonomous vehicles. There are several features of the companys offerings that are attracting attention from automakers, including one major company that has a development agreement. The promises include a durable solid-state device with a single, 1,550 nanometer laser, long range, fidelity and capability to focus on distinct objects in the field.

AEye was one of a select group of companies to take home three Stevie Golds in the American Business Awards , the worlds premier business awards program. More than 3,700 nominations were reviewed by more than 200 judges, whose average scores determined the winners.

Some of the comments included, The best part of the innovation is AEyes iDAR technology which mimics how a humans visual cortex focuses on and evaluates potential driving hazards,

AEyes iDAR software takes an innovative approach to improve the response time of autonomous vehicles sensing capabilities that allows the sensors to make decisions based upon input vs. the traditional method of routing the inputs upstream to a CPU for evaluation, and AEyes work is a critical enabler to safe autonomy which will have an enormous impact on future transportation.

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Vision For Ndt: Thermal Imaging Analysis

The Future of Smarter Technology

Thermal cameras were primarily developed for the military purpose, as a night vision tool for surveillance nowadays with decreased prices, a broader field of applications are opening up.

One such application is augmented non-destructive testing Computer Vision. The solution detects defects and marks the area of interest where there is a high probability for defined defects/anomalies using radiology images taken through NDT techniques.

Automated Vision based inspection widens the visible spectrum and can detect metal surface defects invisible to the human eye.

Figure 5: Machine Vision visible spectrum

Another very interesting applied application of thermal imaging data is to understand how the surface cover of the Peruvian Andes glaciers, shrunk by about 30% in the last few decades, is affecting the melt rate. It poses a serious threat to the water supply of the people living in the Ancash region of Peru. Rosie Bisset, a researcher at Edinburgh University, is mapping the surface of some of these highest glaciers in South America using data from drones equipped with thermal imaging cameras.

Advanced implications of computer vision and deep learning technologies will help such analysis, studies and experiments in upcoming years.

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Synthetic Data Is About To Transform Artificial Intelligence

These people do not exist. These faces were artificially generated using a form of deep learning … known as generative adversarial networks . Synthetic data like this is becoming increasingly indistinguishable from real-world data.

Imagine if it were possible to produce infinite amounts of the worlds most valuable resource, cheaply and quickly. What dramatic economic transformations and opportunities would result?

This is a reality today. It is called synthetic data.

Synthetic data is not a new idea, but it is now approaching a critical inflection point in terms of real-world impact. It is poised to upend the entire value chain and technology stack for artificial intelligence, with immense economic implications.

Data is the lifeblood of modern artificial intelligence. Getting the right data is both the most important and the most challenging part of building powerful AI. Collecting quality data from the real world is complicated, expensive and time-consuming. This is where synthetic data comes in.

Synthetic data is an elegantly simple conceptone of those ideas that seems almost too good to be true. In a nutshell, synthetic data technology enables practitioners to simply digitally generate the data that they need, on demand, in whatever volume they require, tailored to their precise specifications.

According to a widely referenced Gartner study, 60% of all data used in the development of AI will be synthetic rather than real by 2024.

Gartner’s Hype Cycle 2022

Gartner – a consulting firm for IT leaders, uses Gartner’s magic quadrants and hype cycle as their two main data visualization and analysis tools.

While Gartner Magic Quadrants is a research methodology and visualization tool used to evaluate the progress of companies in a technology based market. The so-called Hype Cycle is a graphical representation used and branded by Gartner to represent maturity, adoption, and social application of specific technologies.

.@Gartner_IT released its annual Hype Cycle for Emerging Technologies, revealing key technologies that are enabling the expansion of immersive experiences, accelerating #AI automation & optimizing technologist delivery. Learn more here. #GartnerSYM#CIO

Gartner identifies three emerging technology trends and names immersive technology experience expansion at the top. These immersive experience technologies are metaverse, NFTs, super apps and Web3.

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Why Successful Computer Vision Implementations Are Challenging

While these successful and groundbreaking examples of computer vision applications showcase its ability to transform and disrupt industries, the challenges faced during the implementation process can potentially become crippling. In fact, most companies that invest in computer vision struggle to bring proof of concepts into reality and derive tangible business value from their investments4 .

This raises the question about the ideal approach that should be considered by analytics leaders to preserve the enthusiasm around the value of computer vision and AI in general. A recent study5 supported by Nyenrode and Deloitte, finds that three factors emerge as cross-cutting for successful adoption of AI:

  • Businesses should not engage with AI for the sake of AI but use it to solve a specific business problem.
  • More data is not always better, but rather quality data is needed. For computer vision, in particular, this implies both quality footage, stressing the importance of hardware, as well as accurate and consistent labelling when training models.
  • AIs transformational character requires targeted investment in upskilling and reskilling throughout the organization.
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