Computer Vision in Sports Training: How it is Giving the Winning Edge?

Every four years, the Olympics captures the world’s attention.

Athletes push their bodies and minds to the absolute limit in a breathtaking display of dedication, skill, and raw talent.

But beyond the entertainment, the Olympics also give the sports industry a big economic boost and spark some serious tech innovation.

One such advancement is – computer vision!

During the Tokyo 2020 Olympics, this powerful tech was being used to make the games more exciting, fair, and efficient – from viewing experience to judging and training.

Source

Computer Vision in Sports Training: How Does it Work?

Okay. So, it’s pretty fascinating stuff.

Basically, computer vision uses advanced image and video analysis to help athletes get better at what they do.

Here’s a quick rundown:

1. Image and Video Capture

High-speed, high-resolution cameras are used to capture detailed images and videos.

These can include RGB cameras, depth cameras, and infrared cameras.

Source

2. Preprocessing

• Algorithms like Gaussian blur and median filtering are applied to reduce noise in the images
• Techniques such as digital image stabilization can be used to correct for camera shake
• Methods like Mixture of Gaussians (MOG) or K-nearest neighbors (KNN) are used to isolate moving objects from the background

3. Feature Extraction

Identifying and extracting key features from images or video frames is crucial.

Key algorithms include:

• SIFT (Scale-Invariant Feature Transform)
• SURF (Speeded-Up Robust Features)
• ORB (Oriented FAST and Rotated BRIEF)

4. Pose Estimation

Pose estimation determines the precise position and orientation of an athlete’s body:

5. Tracking and Motion Analysis

Analyzing sequences of movements is essential for assessing performance.

For Optical flow techniques like the Lucas-Kanade method and Farneback’s are being used.

Meanwhile, for Object tracking, algorithms such as the Kalman filter, SORT (Simple Online and Realtime Tracking), and DeepSORT come into action.

Source

6. Action Recognition and Pattern Analysis

Recognizing specific actions and patterns involves advanced algorithms.

Recurrent neural networks (RNNs), particularly LSTM (Long Short-Term Memory), capture long-term dependencies in sequences, while 3D Convolutional Networks (C3D) extend 2D convolutions into the temporal dimension.

7. Data Integration and Feedback

Combining data from various sources and providing actionable feedback is crucial.

Coaches receive detailed reports on player performance and movement patterns, with visual aids highlighting areas for improvement.

Practical Applications of Computer Vision in Sports Training

Computer vision gives athletes and coaches some seriously valuable insights and tools to help them improve their level of training.

By tracking movements, we can spot potential injuries before they happen.

Computer vision helps by identifying risky movements and bad form, allowing us to take action to prevent them.

Real-world example:

The NFL worked with Amazon Web Services (AWS) to use AI and machine learning to build the Digital Athlete, a virtual representation of an NFL player that can be used to better predict and eventually prevent player injury.

Source

Computer vision can analyze athletes’ movements in detail and provide critical insights into technique and efficiency.

Real-world example:

The Leicester Tigers are one of the most successful clubs in world rugby, having been crowned English Champions ten times and European Champions twice.

They utilized Sportscode as part of their video analysis process.

Systems using computer vision in sports training can capture and analyze the biomechanics of an athlete’s movements, such as a basketball player’s shooting form.

This helps in refining techniques and optimizing performance.

Real-world example:

PING, one of the world’s leading golf club manufacturers, is using the Vicon system to push the boundaries of golf performance.

Source

By analyzing footage in real-time, computer vision systems can provide instant feedback to athletes during training sessions.

For example, it can alert a tennis player if their swing deviates from an optimal path.

Real-world example:

With live data of shot speeds, visual ball trajectories, and being the eyes and ears of the outside courts at ATP Masters 1000s, Hawk-Eye has enabled ATP Media to add an extra, insightful layer in telling the stories and sharing the best on-court moments from the ATP Tour to fans on social media.

Source

How Athletes and Coaches Are Embracing Computer Vision Technologies

Athletes and coaches are getting really into computer vision tech now.

They’re using it to up their game, avoid injuries, and make their training more effective. For instance, here’s a real-life example of how it’s being used:

Lead the Winning Edge for Athletes with Our Computer Vision Expertise!

At Azilen, we’ve spent 15 years crafting NextGen software products.

If you’re looking to enhance the sports training experience for athletes, we’ve got you covered with our expertise in:

Our expertise in Data engineering ↗️ and AI development ↗️ enables you to gain deep insights from your sports data.

We use advanced machine learning and deep learning techniques to analyze player performance, predict injuries, and optimize strategies.

From raw data to actionable intelligence, we build custom AI models that make a real difference.

We design cloud architectures that support real-time data processing and analytics.

With platforms like AWS, Azure, and Google Cloud, our solutions are scalable and reliable.

Our DevOps services ↗️ practices ensure smooth deployment and operation of your applications, using tools like Docker and Kubernetes for maximum efficiency.

Our team excels in developing cutting-edge computer vision solutions tailored to sports training.

We create custom models for real-time tracking, movement analysis, and action recognition, using technologies like OpenCV, TensorFlow, and PyTorch.

Whether it’s improving athlete performance or creating immersive training environments, we’ve got the skills to help.

Let’s work together to achieve new heights in athletic excellence.