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Leveraging computer vision to identify animals at risk of injury

  • Sports

Leveraging computer vision to identify animals at risk of injury

OneSix developed a scalable horse workout tracking application using advanced computer vision, multi-camera arrays, and ultra-wideband tracking to monitor key performance metrics and detect early signs of injury.
AI & Machine Learning
Computer Vision

Overview

A data-driven approach to equine health and performance

A leading provider in the horse racing industry sought to develop a Horse Workout Tracking Application that:

  • Identifies signs of injury before catastrophic breakdown
  • Derives critical metrics e.g., velocity, stride length, etc.
  • Scales to 3,000-4,000 unique horses per track per year

These insights will be used to:

  • Improve animal safety and welfare with early detection and intervention for potential issues that may arise for both the track and the animals
  • Provide trainers and owners with detailed performance metrics and enable them to monitor and visualize metrics

Our Solution

A scalable, real-time injury detection system using computer vision

OneSix built a horse workout tracking application that leverages an overhead multi-camera array, on-track cameras, and an ultra-wideband tracking system to monitor and derive critical metrics from workouts.

  • Installed 7–10 trackside cameras across three unique tracks, along with a complete overhead camera array.
  • Developed a robust production pipeline to fully process footage from on-track cameras, ensuring seamless data collection and analysis.
  • Built an injury detection system capable of identifying foreleg lameness with high accuracy.
  • Horse tracklets are projected to RWC using epipolar geometry to derive key performance metrics such as speed, stride length, acceleration, and symmetry.
  • Real-time alerts are sent to veterinarians via SMS within 3–5 minutes of detecting an adverse event.
  • Alerts are integrated into the RH360 app, allowing vets to review cases and provide feedback.

Results

Proven impact and growing demand

The system successfully identified hundreds of veterinarian-confirmed foreleg injuries, demonstrating its effectiveness in injury detection. Its impact has driven strong demand from veterinarians and industry professionals seeking to enhance equine safety.

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