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An Ultimate Guide Computer Vision in Sports 2022

12 May 2022
Computer Vision Sports Detection
Source: Nvidia

In modern business it is necessary to solve tasks fast and accurately with less spending and human involvement, and this is where computer vision comes in.

According to report by Tractica, the largest growth in Computer Vision industry is come in such domains as sports & entertainment, robotics and healthcare. Technology will increase from $6.6 billion in 2015 to $48.6 billion annually by 2022, at a compound annual growth rate (CAGR) of 32.9%.

Take a look to statistics below to evaluate market:

Computer Vision in Sports Statistics
Source: Tractica

Computer vision is a powerful technology which can turn your camera into an up-to-date data generator, provide you speed of work which can't be compared with human.

In this article, we’re going to answer you all questions about CV and how it can be useful in sports industry. We will exactly explain how this technology works and show relevant cases in sports industry.

Table of contents:

  • What is Computer Vision?
  • How does it work?
  • Types of CV models
  • Use cases in Sports
  • How to implement solution?
  • Conclusion

What is Computer Vision technology?

This is one of the fields of deep learning, the main goal of this field is train models with special algorithms to train computer with camera to process visual data, make analytics and sense of this.  

Computer tries to simulate the perception of the visual world as a human. But unlike humans, a computer has the ability to store a huge amount of data and process it quickly, which gives us the unique ability to put many tasks in the hands of the latest technology.

The image below shows how computer vision works in comparison to how humans process visual input.

How Computer Vision Works

How does Computer Vision work?

The main method is based on special image processing units that accept visual input - it can be a photo or video, after which the model itself begins to work. The model is specially trained, it can predict or return concepts or labels. Perceiving an image at the pixel level with the help of algorithms helps the computer quickly read the "code" of the picture and give it meaning.

Now let's explain in more detail how visual data is processed.

The basic principle is based on algorithms, unlike humans, a computer cannot understand the semantic meaning of an image, but this is replaced by pixels - the numbers that the computer works best with. When a picture is detected, it is presented in an array of pixels, which have basic RGB colours in varying degrees of intensity. We have red, green and blue.

When pixels are combined, they turn into a picture. Now we can safely say that each photo is a matrix.

Below you can see how computer understand images:

Computer Vision Processing

How do we train models? Training differs only in the frequent repetition of huge data sets.

As the computer processes this data, it begins to notice similarities - patterns of objects. For example, in order to teach our computer to identify a cat, we have to send it a numerous of photos of a cat. The model will begin to analyse the photo, look for patterns. After that, the interesting part of training the model begins - they show it, for example, a dog, so that the model learns to distinguish classes that are similar to each other.

Computer Vision Labeled Photos

Types of Computer Vision models

In previous section we told you about creating Computer Vision models, and now we will show you different types of models depending on their purpose.

Image Classification - a basic and very important type of model that is used to solve many complex computer vision problems. Without image classification, it would be impossible to create a model that would capture people in real-time and quickly divide them into groups by gender or age. Using this type, a certain group of images is provided with its own classification, the model needs to learn how to classify visual data using this group.

Image Classification Computer Vision


Object Detection - 
this model is trained to instantly find certain objects of the class. The use of this model can be very wide, for example, in sports we can detect objects such as gates, balls, skis, rackets and much more, which can help us quickly and accurately analyze and collect events Also, a striking example of a detection object can be a face detector, which determines not only a person's face, but the location of the eyes, nose and mouth.

Object Detection Computer Vision


Image Segmentation
 - the task of this model is to divide the image into parts. The computer can distinguish objects and does not need to see them completely. For example, the model can distinguish the object from the background, or other objects, but in the same photo.

Image Segmentation Computer Vision


Video Motion 
– technology allows to analyse body movements and trajectories of objects or animals. This is a key model that is used in sports, with it you can combine various complex tasks - detect and classify objects, determine events and estimate poses. The complex of all these actions is ideal for sports, but is also used in many other areas. For example, medicine - the ability to track microorganisms, which later helps to make more accurate diagnoses. Or agriculture - detection of pests, dividing them into classes and further elimination.

Video Motion Analysis


Face Recognition
 – unlike object detection, the target of this Computer Vision model is a human face. But an ordinary human face is not enough - the model is able to divide people into classes. For example, if you are doing sports analytics, it will be important for you to be able to recognise each player on the field. This is where faсe detection can help you. You just need to upload photos of your players or customers in the store, and your model will be able to clearly determine what actions they perform. And along with the creation of notifications, you can easily receive a message like "Andrew scored a goal!" or "This is your regular customer, give him a discount" :)

Face Recognition

Computer Vision use cases in Sports

In this part of the article, we will show you the classic application of typical computer vision models in sports. Let's skip the typical cases, such as face or object recognition, and move on to more specific ones.

  • Training analysis - used in many professional sports to show the correct execution of movements. An example would be athletics, martial arts, yoga or gymnastics where technique is very essential.
  • Player activity monitoring is an indispensable aspect when using computer vision in sports video analytics. With the help of the initial data, you can draw conclusions about the activity of various players, determine their movement and actions.
  • Trajectory analysis - all sports have an impact with a certain object - the ball, puck, shuttlecock and the analysis of its movements is very important for further analytics of quality or predicting the game.

Now it’s time to get acquainted with specific cases exactly for sports industry.

Real-time action analytics

Real-time video analysis converts parts of video content into a collection of photos, further analysing them behind the usual pixel matrix method that we learned a little above.

Fast response and video processing helps you get up-to-date and accurate information right at the very moment when you are standing at the playing field. Even if you might not notice something, be sure that the model did everything for you and after recording you will be able to analyse the most accurate computer data. You no longer need to make regular video recordings of the game and re-watch it to make analytics.

The use of video analytics is very popular in sports clubs or arenas, as human errors and inattention will no longer be allowed here. Computer vision model will accurately determine whether the ball was landed or who made a goal.

Take a look at our football video analysis project. The camera has a built-in program for analysing the video stream of the game in real time:

Below we would like to present you our Computer Vision use case. Our model monitors the game and is able to identify objects, events and people.

Videoanalytics Football


Professional sports team analysis

In professional sports, the strategy and behaviour of players is extremely important. Having received a high-quality and fast analysis using computer vision models, you will be able to improve your players, determine their strengths and weaknesses. You do not need to spend several weeks processing videos for analysis, you immediately get a detailed description of all game events and the movements of your players. In addition to player movements, models can be developed to focus on objects by determining their trajectories.

Stroke Analysis
Actual in sports such as tennis or ping pong. The computer vision model is capable of detecting strokes and dividing them into categories. For example, when analysing a video, the model differentiates tennis shots as forehands or backhanders. This kind of analysis will help your players hone their professional skills and get better. Such an analysis will help your players hone their professional skills, and also warns of possible violations of the rules of the game.

Ball Trajectory Computer Vision Detection


Pose estimation in mobile applications
Using the latest technology will encourage your users to engage with your application on a daily basis. How often have you seen apps that explained how to do exercises correctly with videos? Probably quite frequently in recent years. And imagine if you create a model that automatically fixes the correct position, counts the number of approaches completed and gives advice on how to improve your workout. An amazing replacement for a real coach. With this type of application, training will always be available, it is enough to have a camera with you.

Develop your specialty - add your own unique moves and poses, become special in your niche without the additional cost of human trainers.

This technology is especially useful for developing your specialty - unique movements or postures. You can teach your programs without the additional cost of real trainers.

Take a look at our developed machine learning models:

Pose Estimation Yoga


Player performance analytics

Coaches should always be concerned about the quality of their sessions and training plans. Everyone in sports should think about the perfect performance. Team sports require proper organisation of the game and an individual approach to training each player. For training and growing up real sports stars.

Prevention of life-threatening situations

In any sport, the safety of the players is important, as it is a constant direct work with body movements. Racing is a prime example of precautions in sports. The computer model above can be trained in such a way that it can determine the health of the equipment. This is also used in other industries, such as construction or manufacture. A huge number of models of equipment, machines or any other objects are loaded so that the computer remembers the correct state, shape or behaviour of equipment in order to prevent disasters and malfunctions.


Sports content & journalism

Using computer vision technology in a team with artificial intelligence, you can achieve exciting results in terms of content.

The model is able to analyze events, such as a goal, and automatically bring the camera closer to the most interesting moment. Imagine, you no longer have to hire a huge number of reporters and wait for post-production to publish sports events, you just need to place several cameras that will intelligently and automatically be able to focus on the really important moments of the game.

The ability to automatically comment on events will soon spread, which will turn the whole world of computer competitions upside down.

Sports Journalism


Fan mood

The variety of applications for computer vision is truly amazing. In the past, experiments were often made with the connection of special wires to track impulses, which made it possible to determine the satisfaction of a person when watching something. Now, with this AI technology, we no longer need to put every viewer in a lab.

Film fans and get a detailed analysis of their satisfaction. Algorithms are able to distinguish many emotions - happiness, boredom, excitement, disappointment, etc.

Fan Engagement Detection Computer Vision



How to implement?

Server Application

This type of installation allows you to host a machine learning model on servers like Azure, AWC, Digital Ocean without the need for coding. The model is provided via API. After you perform the necessary technical settings, send a video or photo, after which you receive a response from the model with all the information about the object.

This is the simplest solution that works through integration.

Edge Application

Processing photos and videos on the server takes some time, so you will receive results with a certain waiting time. If you need to get data quickly, there is an option to install the model on the hardware. These can be edge devices such as Raspberry Pi, Google Coral, or NVidia Jetson.

This type of setup is ideal for real-time analysis because it does not require sending to the server, so there is no waiting for processing.

What are the data sources?

  • Photos
  • Videos
  • RTSP video streams
  • Equipment video streams.

Conclusion

Computer vision is the most popular technology topic and its demand is only increasing. This is a new look at data processing and the way it is perceived, we finally taught computers to see. Every day we create a large amount of data that we can successfully use to train models, and they, in turn, will serve as a hopeful assistant in solving our business challenges.

We have been working with this technology most often in recent years and by leveraging strong machine learning expertise, we handle all data-related processes, including data collection, model training, model deployment on cloud, edge or mobile. We try to make all stages of introducing technology into your business simple and fast, so that you immediately feel all the benefits of using the latest technologies.

We will take into account all the individual characteristics of your business in order to create a really necessary and effective computer vision model that will eventually become indispensable in your daily routine.

Contact us to get professional advice and further start creating amazing digital changes.

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