4 Computer Vision Applications in Sports (2022 Use Cases)
4 Computer Vision Applications in Sports (2022 Use Cases)
In the business world, it is necessary to solve tasks quickly and accurately while spending less money and involving fewer people, which is where computer vision comes in. According to the researchers' analysis, the most significant growth in the Computer Vision industry is in sports and entertainment, robotics, and health.
Computer vision is a fantastic technology that can transform your camera into a modern data generator and provide you with work speeds that humans cannot match. You can click here to read our article about what computer vision is.
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In today's blog, we will discuss What Computer Vision is used for, how it can be helpful in the sports industry, and its sample applications in 2022.
Let's start with what computer vision is used for.
What is Computer Vision Used For?
Computer vision (CV) is an artificial intelligence (AI) subcategory focusing on developing and deploying digital systems to perform, analyze, and analyze visual data. Computer vision enables computing systems to correctly identify and act on an object or person in a digital image.
Convolutional neural networks (CNNs) are used in computer vision to process visual data at the pixel level. Deep learning recurrent neural networks (RNNs) are used to understand how one pixel corresponds to another.
Below is an essential list of computer vision uses:
- Biometric access management: Computer Vision plays a vital role in face and iris recognition and has different application structures in various industries. For example, for industrial robots and driverless cars, computer vision is an essential tool that enables robots and autonomous vehicles to avoid collisions and navigate safely.
- Diagnostics on the computer: Computer Vision can be used with other types of AI programming to automate the analysis of X-rays and MRIs.
- Augmented reality: The CV is critical for mixed reality programming in determining where a virtual object should be placed.
- Manufacturing: It aids in the automation of quality control, reducing safety risks, and increasing production efficiency.
- Retail: It prevents customers from waiting in line by self-checkout and reduces the workload through automatic replenishment.
You can check out our Computer Vision Solutions article, where we examine sectoral application examples.
Let's move forward to our next topic and explain What Computer Vision is Used for in Sports?
What is Computer Vision Used for in Sports?
Artificial intelligence was virtually unknown in sports just four years ago. However, deep learning and CV are now finding their way into a variety of sports industry applications. Regardless of whether it is used by broadcasters to improve the spectator experience of a sport or by clubs to become more competitive and successful, the industry has significantly increased its adoption of these modern techniques.
In many most-known sports fields, it can be time-consuming and challenging for coaches and analysts to monitor and analyze matches in great detail. AI and CV are successful ways to increase data collection.
Consider only the limited number of video footage angles available to a performance analyst during practice or specific matches, especially those that haven't been televised. Unfortunately, these images are limited to visualization of the player's movement rather than detailed analysis. Please note that the data and insights from the found footage require the analyst to spend hours manually jotting down and collecting data while replaying the video.
In situations such as this one, computer vision techniques can fill the gap between the sports game and analytical insights by providing innovative ways to collect data and obtain precious analysis via automated systems that locate and segment each player of interest and follow them throughout the video.
Computer vision systems can now distinguish between the ground, players, and other foreground objects thanks to image processing. Methods such as color-based ground elimination in courts with uniformly colored surfaces enable computer vision models to detect pitch zones, track moving players, and identify the ball. Color-based segmentation algorithms, for example, are currently used to detect grass by its green color and treat it as the background of an image or video frame in which players and objects move in front of it.
Computer Vision Applications in Sports
1. REAL-TIME ACTION RECOGNITION IN HOCKEY
After deep research on efficient recognition of actions in hockey, the researchers created the article. The main idea was that experts had come up with a complex, multi-component visual data processing model called the Action Recognition Hourglass Network (ARHN).
In simple words, the complex algorithm takes a piece of animated video content and turns it into a series of images. Another basic algorithm in the Stacked Hourglass network then analyzes the players' positions (straight and cross skating, pre-, and post-shot poses) and categorizes them.
These models have long been used to help give the fairest and most accurate scores in this and other types of sports.
2. BALL TRACKING SYSTEMS IN TENNIS
Since the mid-2000s, precise tennis (as well as badminton and cricket) ball trajectories have been tracked in sports. As a result, specialized systems focus on multiple objects in the image with the shape of a ball. When these are detected, a 3-dimensional trajectory is constructed by connecting the ball movement pattern frame by frame.
Multiple camera angles and flexible motion capture are critical in this situation. The main goal is to specify whether the ball landed in or out of bounds during the game. The underlying algorithms can predict ball trajectories based on various conditions (a player's miss on their deepest, most complex layers.
3. PREVENTION OF LIFE-THREATENING SITUATIONS
Computer vision is used in NASCAR racing and other sports where players face potentially life-threatening situations to detect and prevent vehicle malfunctions. Such systems, in essence, save lives in this situation.
Massive Big Data-based databases of vehicle models are commonly used to recognize specific cars, analyzing them in minute detail during the event. As a result, experts have real-time access to the car's interior to detect any minor flaws that could lead to severe consequences.
Nowadays, sports video analytics systems have become a very profitable business for many sports arenas and clubs. Even though the development of such systems necessitates the synchronization of dozens of cameras as well as powerful computing capabilities, the effort is usually well rewarded in the long run.
4. SMART SPORTS JOURNALISM
Computer vision can also create visually appealing media content and, more precisely, report on game highlights.
Instant focus on the most exciting events available by analyzing the most noticeable, dynamic actions occurring in the field of tracking, ringing, etc., based on some of the algorithms mentioned earlier.
This is a critical capability for live events to keep all spectators on the edge of their seats. Aside from visual features, AI can also help to automatically commentate on events without the assistance of live- speakers.
We emphasized the importance of AI in sports above; we now know that in the long run, it makes refereeing, analyzing, highlighting, and satisfying fans easier and more efficient. However, in implementing an AI-based system for sporting events, you have the ultimate choice between going for known but costly solutions or ordering a cost-effective custom local system.
Artificial intelligence platforms such as Cameralyze provide a way to rapidly build, deploy and scale computer vision applications in sports. Powered by the end-to-end computer vision platform Cameralyze, it helps companies secure sporting events and improve the performance of athletes.
Get in touch with us and learn more about how your organization can successfully and quickly deliver real-time computer vision in sports.
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