Computer vision is one of the most technologically advanced fields for artificial intelligence. This is going to make a lot of money for businesses.
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What is Computer Vision?
Computer vision is the root of the “deep learning” movement, which began in the 1990s. At the now-historic ImageNet competition in 2012, Geoff Hinton and his team showed off a neural network, which was new at the time. Its performance was better than any other computer-based image recognition system to exist until that date. An era of deep learning began with computer vision as its first use case. It’s been a decade, and computer vision has come a long way.
In other words, computer vision is the mechanization of what we see with our eyes. It’s a critical sense for a large number of businesses. It is prevalent in many aspects of daily life and business. It can be done automatically offers up a plethora of new market possibilities across the board.
The first wave of business in current computer vision focused on making cars that could drive themselves. However, several startup success stories in that field, like computer vision pioneer Mobileye’s $15.3 billion sales to Intel in 2018, show how the technology can change markets and make a lot of money.
Today, computer vision is used in every part of the economy. From agriculture to retail, from insurance to development, computer vision is being used in a wide range of industry-specific applications with a lot of growth potential.
Computer Vision and Future of Startups
In the next few years, there will be a lot of “unicorns” that will be earning colossal revenue and making names with computer vision. Shortly, many high computer vision companies will break out into the commercial world and become well-known to the general public. Many people are excited about the technology’s journey from survey to market at this point, and it’s also crucial.
Sports
Artificial intelligence was almost unknown in sports only five years ago, but deep learning and computer vision are already used in sports. Whether utilized by broadcasters to improve the fan experience or by teams to improve their competitiveness and success, the industry has intensified its use of new tactics.
Player tracking is a major goal of computer vision in sports. This entails locating all players at a particular time. Coaches may use player monitoring to rapidly analyse how individual players move on the pitch and how the squad as a whole is formed. Today’s most powerful sports computer vision applications employ automatic segmentation to recognise player-like areas.
With JOGO, we’re able to gather data on the performance of young athletes like never before. We can get a complete picture of a player’s performance from the 200 data points acquired at each point in time. JOGO shows opportunity for progress and growth, and where others stop, JOGO goes on. We make it simple for players and their coaches to improve their game by offering personalised feedback and ideas based on their specific requirements.
Agriculture
People worldwide work in agriculture, one of the biggest and most important jobs. Today, decisions about how, when, and what to grow are not very well thought out or precise. There is a chance to make a massive difference in how food is made using visual data and machine learning.
Growers can use computer vision systems to get real-time information about how their chemical inputs should be used, how they can improve their farming operations, and how their yields will grow. This information comes from aerial images taken by satellites, drones, or planes.
The analysis can help determine which crops need somewhat irrigation, where pipe leaks and pressure failures are hurting crop growth, which areas need more or less fertilization, which fields have poor pest and disease control, etc. AI systems can decide things a lot faster, more reliably, and more quickly than humans can do independently.
Ceres Imaging, Prospera, Sentera, and Hummingbird Technologies are some promising ag-tech start-ups interested in these opportunities.
Retail
Computer vision can be used in a lot of ways in retail. Perhaps the most appealing of these options is shopping without paying for it. As soon as a store has the sensors and computer vision systems needed, shoppers can enter, pick up the items they want to buy, and walk out without waiting in line. They’ll also get an automated receipt for their visit.
When Amazon launched its Amazon Go program in 2016, it was the first to make shopping without a checkout line possible. A few start-ups are looking into this right now, like Standard Cognition, Grabango, Trigo Vision, Zippin, and AiFi. Standard Cognition, the company with the most money, announced that SoftBank’s Vision Fund had invested $150 million in the company this month.
The experience of people who go shopping in person will never be the same again, says Grabango CEO Will Glaser. As soon as you put something in your cart, computer vision systems like Grabango’s can see it. This means at the end of your shopping trip; there is no need to re-itemize everything you bought. So grab, go, and get on with your day.”
In addition to giving customers a better experience, click and collect shopping will help retailers save money and fight shrinkage.
Another important use of computer vision in retail is inventory management. Retailers have difficulty figuring out how to put the right products on the shelves and keep the aisles stocked all day. As a result, retailers lose money each year because they don’t have enough products on their shelves. Focal Systems is an exciting company that uses computer vision to make inventory management easier and cut down on out-of-stocks.
Insurance
The insurance business relies a lot on looking at things to precisely price and recapitalizes policies, for example, and to figure out how much damage there was after an accident for claims reasons. Using computer vision can help with this visual analysis the same way it can help with other things. Moreover, this can be done faster, cheaper, and more precisely than now.
Cape Analytics and Betterview are two companies that use computer vision to help people get insurance for their homes. Use geospatial data to automatically figure out what kind of building it is, how well the roof is in shape, how much yard debris the estate has, how close it is to vegetation, and hundreds of other things that make up a property’s risk profile and how much insurance it needs.
Computer vision systems can do this analysis right away, at a large scale, and quickly, based on decades of data. Compare this to the way we usually do things now: send a person to inspect each property in person, one at a time.
Another company to watch in this category is Tractable, based in London and uses computer vision to make instant damage projections after car accidents and natural catastrophes. These AI-based estimates speed up claims processing and cut down on human errors.
Constructions
Construction is a big business that has been underdeveloped in terms of digitization. Computer vision can increase productivity and reduce costs in the building industry. In response to these possibilities, startups have sprouted up, creating a dynamic ecosystem.
To keep an eye on building sites from above, TraceAir employs drones to capture high-resolution aerial photography. Disperse uses computer vision to create “digital twins” of building sites in progress. Finally, 1build uses computer vision to read floor plans, material schedules, and architectural features on blueprints to automate cost estimates in the building.
“Cost estimates in construction effectively imitate the whole building process,” said 1build CEO Dmitry Alexin. Alexin. As a result, building businesses now have “atomic-level insight” into their expenses thanks to “machine learning and computer vision,” says the author.
Security
Visual surveillance is critical to physical security. After all, the camera is the most pervasive security gadget. As a result, a natural opportunity emerges to use computer vision to enhance the robustness and reliability of physical security.
Numerous firms are using computer vision in novel ways to improve and automate the physical security industry.
Verkada provides an AI-enabled commercial property protection system that leverages hardware sensors, computer vision algorithms, and an integrated software platform. Last year, the firm was valued at $1.6 billion, making it one of just a handful of computer vision companies to earn unicorn status.
Deep Sentinel has developed a system comparable to this for home security. The business employs a deft human-in-the-loop technique that enables human security officers to respond remotely in real-time through a microphone when the AI system identifies a danger.
“Computer vision is fundamentally altering the way physical security is conducted,” stated Deep Sentinel CEO David Selinger. “Our artificial intelligence technology serves as a tool for reducing distractions, highlighting critical data, and determining which human guards are appropriate for each circumstance. Our AI is more precise and responsive than any human being—and it never loses focus.”
Another area of security where computer vision might be beneficial is at checkpoints, such as airports, sporting events, and government buildings. Human staff who are tired and inattentive often overlook dangers at these checkpoints. Computer vision may be used in cameras or X-ray feeds to identify harmful things more accurately and reliably than a person, increasing public safety.
Last year, Palantir purchased Synapse Technology, a promising firm researching computer vision solutions for checkpoint security.
It is critical to recognize that the deployment of computer vision in security settings may, and often does, cross the boundary between safety-promoting monitoring and excessive surveillance. The use of face recognition technology by governments to follow and monitor civilians has sparked significant outrage worldwide. For example, computer vision has purportedly been used in China to repress the Uyghur ethnic group.
Conclusion
Computer vision may be utilized for destructive and beneficial purposes, as with any sophisticated technology. As a result, regulators, corporations, and people must ensure that society uses technology responsibly.
Computer vision will provide exciting potential for companies. This is due to the breadth of uses for computer vision technology. We discussed the vehicle sector and robotic applications before. The possibility of computer vision is enormous, creating chances for entrepreneurs to continue participating.
This is a challenging field, but the top slot is still available. While there are prominent companies in this industry, there is no market leader. Given the interdisciplinary nature of computer vision, this may remain the case for some time. As a result, there are chances for new entrepreneurs to enter the industry and compete with established firms.
