High-Speed Ball Tracking for Squash Analytics

DCube developed a high-speed computer vision system for detecting and tracking squash balls in near real time across multiple court environments. The solution enables advanced match and training analytics by accurately capturing ball trajectories during live games and practice sessions.

The Client

Client Name: LiveSports Inc.
Industry: Technology
Region: US
Company Size: Enterprise

The Challenge

Squash ball tracking is technically challenging due to the ball's small size, extreme speed, motion blur, and frequent occlusions. Variations in court types, lighting conditions, and camera angles further complicate reliable detection and real-time performance requirements.

The Solution

DCube designed and trained deep learning–based computer vision models optimized for high-speed object detection and multi-object tracking. The system was engineered to perform near real-time inference while remaining robust across different court settings, match formats, and training scenarios.

Key Features

• High-speed squash ball detection using deep learning
• Multi-ball tracking for rallies, drills, and gameplay analysis
• Robust performance across varied court types and lighting conditions
• Near real-time inference for live analytics use cases
• Scalable architecture for training and broadcast environments

Technologies Used

• Deep Learning (Computer Vision)
• Python

Results & Impact

• Enabled detailed rally and performance analytics for players and coaches
• Improved accuracy of ball trajectory and speed measurements
• Supported live and post-match analytical insights
• Reduced manual annotation and analysis effort
• Laid the foundation for advanced sports intelligence features

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