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Computer Vision (CV) definition is simple as it reads – the ability of computers to see. With this ability, great opportunities open up for many industries – from video surveillance theft detection to complex analysis of human health.
Artificial Intelligence technologies come in our daily routine, do you remember times then we haven't voice translators or Face ID on smartphone? Sometimes we don't notice how quick we implement new technologies in our life, but knowing the trends and being innovative is the key to success.
Health is our #1 priority, that's why the global market Computer Vision in Healthcare is projected to multiply 9 times by 2026 to $2.4 Billion. That means, we must be prepared to implement this technology in our business and life anyway, because every single day it expands new industries and usage for.
In this article, we will dive into Computer Vision definition, explore its algorithms and applications to see how it can be useful, so let’s get started!
Table of contents:
To understand the concept, try to imagine yourself in childhood. When a child is born, he doesn't know about anything and start to explore the world with books, television, videos and parents, which explain them the meaning of each object around. Then, this processes repeats, child have his own experience and memory. After a long learning time, child can identify a various object and explain for what they need.
In this case, computer is a child, who wants to "see" and "understand" the world around him. We realize his desire with the help of large datasets.
But the main difference between human and computer is that the last can't make decision based on experience or recognise an object without prior training. Now it's coming with artificial intelligence progress.
Computer vision is a field of study focused on the problem of helping computers to see.
At an abstract level, the goal of computer vision problems is to use the observed image data to infer something about the world.
— Page 83, Computer Vision: Models, Learning, and Inference, 2012.
As we mentioned above, the computer vision model is trained with thousands and thousands of pictures that have names, characteristics, and descriptions.
Using deep learning algorithms, the computer vision system learns to detect, classify and recognise objects.
We send a million photos of a cat, the computer analyses them, identifies the similarities of all cats - in shape, colours, the muzzle location, and at the end of the process creates a “cat” model. In the future, the computer will be able to detect a cat in any photo.
If you want to learn more about how algorithms work, check out Golan Levin's article for a technical explanation of the learning process. Briefly explained, a machine perceives an image as an array of pixels, which have their own values from 0(black) to 255(white). This colour representing system is called RGB.
The way of learning is based on patterns, it’s when we try to find something similar between different objects - colour, shape, volume. It was inspired by the popular hypothesis of the human perception.
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Our brain makes a lot of processes at the same time to understand an object, identify face or understand action. We don't set apart it to different processes, because it happens automatically. But when it comes to machine learning, we face a huge number of computer vision algorithms, so let's go through them.
Object classification - this technique helps to classify objects on an image. For example, we have 2 types of animals at the photo, this algorithm distinguish cat from dog.
Object detection - computer vision method for identifying object, also their location within an image.
Image segmentation breaking down an image into various subgroups, it includes several types.
Face recognition - is subtype of object detection, helps to identify face by using keypoints.
Pose estimation(Keypoints detection) - method to identify human pose by joints. For example, we need to make sure someone does sports exercise correct - model is checking position of hands and legs by keypoints.
Action recognition - usually used for detecting theft, fights or suspicious behaviour in real-time. It helps support law and order in crowded places.Â
Object counting most used in retail or event industry. It's great way to make insights by knowing how many people is coming to your store or event.
Image transformation - computer also can help to make changes on an image, there are several types of transformation.
3D object reconstruction - it's feature when you want to recover 3D shapes from single-view RGB image.
Deep fakes - the way to make fake by replacing a person face in an existing image or video.
OCR (Object Character Recognition) - converts any type of image which contains written or printed text into a machine-readable format.
It's not that easy to implement new technology without preliminary preparation and experienced computer vision engineers. Let's check out challenges face within implementing cv.
Hardware quality - it's important to have good hardware to reach your goals, because this technology combines software and hardware. Camera is for eyes, algorithms is for brain. Imagine you have bad eyesight, can you distinguish one person from another standing far away from them? Exactly the same happens when you try to implement computer vision model into bad quality camera.
Here is great example by Walmart, they have shelf-scanning robots walking around. If you try to scan products on shelf from above, that would be not effectively from the point of accuracy.
Incomplete datasets - machine needs to be provided with complete data, unlike a human who can identify objects logically. We can close our eyes for lack of data and full it by our experience.
Datasets processing - when it comes to datasets, you need to be sure computer will understand images correct. There are a lot of factors can affect on this process - shadows, colours, noise. Also, there might be not enough data for making model. For example, healthcare. It's difficult to have a thousand photos of fluorography, because it can be private and are not shared by hospitals.
Computer Vision Engineers - a company will need a team of experts in ai and machine learning to adopt system into business. It's real to find good engineers to work with technology in its early stage, just choose software development company which have expertise and it's desirable in your industry.
The main pain in this industry is injury costs, because construction site is very dangerous. Danger is everywhere - high, debris, vehicles, and ignition. To make sure employees in safety and to detect accidents, cameras have been installed in such places for a long time, which was used by the computer vision engineers. PPE detection - hats, gloves, masks and all types of equipment is already available to implement in cameras.
Check out our previous article about employees safety in construction to get more examples.
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It's already a thing of the past to hire people for quality assurance at the manufacturing. Accuracy of machine eye is incredible and helps save costs for employees and non-quality products. It was an impressive step to teach complicated machines to do simple tasks, because now we can do our real work, without any routine. Installed cameras on manufacture can detect defects and alert about it employees. Or another case - combine it with robotics to immediately eliminate defect by robots hands, finally you get a real employee, who will never miss the eye or get sick and tired.
In retail everything is spinning around customer, that's why it's critical important to know who is your customer exactly. Already installed cameras in store can be your marketing helper to understand customer journey and behavior.
Heat maps and counters helps retail to figure out where it's better to put product and how many visitors you have per day.
Face recognition make great insights about you customer - age, gender and his preferences.
Inventory management by using robotics can handle your shelf management and alert employees about lack of products, wrong location or price accuracy.
Here is an example, Sam's club, an American retail company is using robots.
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The vital industry agriculture hasn't taken back seat in using new technologies. Control of quality of fields from bird's eye by using drones, making analytics and predictions to take care of the sowing and prevent crop loss. Livestock farming, crop monitoring and autonomous harvesting already realized and using in agriculture these days.
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Given the stiff market competition in real estate industry, it becomes challenging to stand out. Computer vision in retail can be an instrument of incredible customer experience. Uploading tons of apartments photos, choose perfect and describing it by hands can be long and boring. CV technology analyse photos, translates visual data into keywords and parameters. Model can be trained to detect object like air conditioner, furniture, any kitchen appliances and automatically add parameters.
Do you remember when we use sport mobile applications to do exercise? It was difficult to understand without trainer and instructions how to do them correctly. But now by using pose estimation algorithm it's easy to do sport, all you need is to turn on camera and have fun with your virtual instructor.
Also, Computer Vision in sports is useful in analytics. Hours of recording soccer or basketball need to rewatch takes a lot of effort. By implementing models into camera on field you can get game results immediately.
The main benefit of using CV is the accuracy compared to human, who can be sleepy, tired, not having excellent memory and reaction.
By right combination of hardware, software and engineers, business can enhance consumer experience, reduce costs and increase productivity.
Computer Vision task is to automate processes, to free a human from routine or difficult tasks. Explore new possibilities of vision, from birds eye or microorganism world.
Get surveillance 24/7, don't miss single event and stay up to date by reports.
If you are interested to implement computer vision in your business, there are several tips how to get started with computer vision:
Computer vision is changing most industry verticals, it can solve business problems and reduce costs only by using already existing cameras. You can develop a perfect solution matches all your business requirements with a reliable and experienced partner.
We at ByteAnt have Computer Vision use cases and ready to deploy models in various industries. Also, our team have expertise in Data Science, AI, Machine Learning and CV to achieve and realise all your ideas.
Contact us to start developing your solution and experience all the benefits of the newest technology.
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