Computer Vision Subsystems for Racket Sports products (Tennis, Padel)

Our client is an international company operating in the sports analytics and broadcasting segment, specializing in racket sports, including tennis and padel. The company provides a technological platform for video broadcasts, in-depth match statistics, player analysis, and automated content production.

The client was already using cameras to record matches but experienced difficulties with accurate ball and court line detection, as well as automatic recognition of game events. A comprehensive computer vision system was needed that could operate in real-time and adapt to the dynamic conditions of padel courts.

Problem / Task

The main tasks set by the client were:

• Accurate detection of markings and lines on the court with partial overlaps and glares.
• Tracking the ball and players at high speed, including during sharp changes of direction.
• Recognition of game events: serve, out, bounce, change of sides.
• Tracking and identification of players even when they go out of sight.
• Support for padel-specific cases, including a player changing clothes during a match.
• Real-time operation and compatibility with the client’s existing video stream.

Solution

We developed and implemented a modular CV system, including:

• Calibration and semantic detection of the court and markings (including dynamic detection when changing cameras).
• High-precision ball tracking with 2D/3D trajectory reconstruction.
• A separate module for bounce detection, taking into account the surface (glass, net, carpet).
• Recognition of the type of hit based on pose estimation + trajectory.
• Segmentation and pose estimation of players on each frame.
• Automatic identification of players, even after going out of the field and changing clothes.
• Event detection: out, serve, rally, end of point.
• Microservice architecture with REST/WebSocket API for integration with the client’s platform.

Process

The project lasted 7 months, and the stages included:

• Researching the specifics of padel games and preparing custom datasets.
• Training models for various lighting conditions and camera configurations.
• Validating the accuracy of tracking and event recognition in manual tests.
• Integrating modules with the client’s production stack via CI/CD.
• Supporting deployment on edge devices with low latency.

Result

• Up to 95% accuracy in tracking the ball and players, including cases of going out of the court.
• Detection of bounces and outs – accuracy above 92%.
• Recognition of game events with a delay of less than 200 ms.
• Fully automatic identification of players, even when changing clothes.
• Reduction in the time for preparing match reports from 3 hours to 10 minutes.
• Integration without stopping the platform’s operation and with the preservation of all video streams.

Features / Challenges

The project required:

• Solutions to non-standard padel cases: players leaving the field and returning.
• Identification when a player changes their appearance (e.g., taking off a shirt).
• Working with reflections from glass walls, unstable lighting, and glares.
• Models adapted to the conditions of both outdoor and indoor courts.
• All analytics work on the client’s side without transmitting video to the cloud.

Interesting Facts

• To train the models, we manually marked up more than 100 padel matches.
• The system successfully recognizes moments when a player takes off their shirt and returns to the game.
• Using pose estimation helped predict not only the type of hit but also the probability of winning the rally.
• The modules are used to automatically create highlights and match clips.

Feedback

“Complex recognition tasks in padel have been solved effectively. The system is stable, accurate, and does not require operator intervention. Special mention should be made of the reliable operation in unstable shooting conditions and excellent integration with our platform.”

Mykhailo Smorodin

CEO at Paradigma.ST, Computer Vision and AI in Sports Tech

Motivated and entrepreneurial software engineer with a passion for artificial intelligence, computer vision, and sports. Skilled in developing innovative solutions and driving projects from concept to completion. Eager to leverage my technical expertise and business acumen to create impactful advancements in technology.