AI / Computer Vision Systems for Horse Monitoring and Behavior Analysis in Smart Stables

The client is an international company specializing in management and monitoring solutions in the equestrian field. The main task is to digitize the infrastructure of modern stables: from video surveillance to analyzing horse behavior using AI. The platform is aimed at professional venues, elite clubs, as well as individual owners. The client already had a basic system of sensors and cameras, but needed integration with a centralized management system, expanding data analysis and visualization capabilities.

Problem / Task

The client faced several challenges:

• Disparate data sources (cameras, sensors, microcomputers) without a single point of control
• Difficulties in analyzing video streams: inability to automatically interpret horse behavior
• Lack of personalized interfaces for different users (trainers, administrators, owners)
• No convenient access to data for internal data scientists
• Need for a reliable system for storing and viewing video archives

Solution

Our team implemented a modular monitoring and analytics platform with the possibility of expansion:

• Centralized data storage with synchronization of video and sensor data
• Customizable dashboards for different roles (trainer, owner, admin), adapted for tablets
• Analysis of horse behavior: monitoring sleep, activity, behavioral anomalies
• Built-in Python environment (Jupyter) for data scientists with access to video data and metrics
• Flexible system of graphs, reports, and notifications
• Support for both online streaming and archived viewing

Process

The project was implemented in 6 months:

• Audit of the client’s infrastructure and architecture planning
• Collection and normalization of data from microcomputers and cameras
• Development of backend API and visual interfaces
• Training of horse behavior models on video data
• Testing on a pilot farm with remote control
• Deployment of the system at the client’s site and adaptation to real conditions
• Support and CI/CD pipeline for regular updates

Result

• 87% accuracy in detecting abnormal behavior (lying down, aggression, anxiety)
• Up to 50% reduction in missed problem cases due to the notification system
• Horse condition analysis time reduced from 1 hour to 5 minutes
• Dashboards are used daily by more than 15 users from different devices
• Connecting new sensors and cameras does not require developer involvement
• The client was able to use the system to prepare veterinary reports and plan training sessions

Features / Difficulties

• Working with low light and unstable network — buffering and edge-processing were implemented
• Heterogeneity of data from different types of sensors — an adaptation and validation system was applied
• Horse behavioral patterns require individual analysis — hybrid models were used
• Emphasis on interface simplicity while maintaining in-depth analytics
• Security and privacy: all data is stored on the client’s local servers

Interesting Facts

• Some models were retrained for each breed, taking into account behavioral characteristics
• The system identified a rare case of colic at an early stage, which helped save the animal
• Trainers use activity heat maps to plan workload
• The Python environment is used to generate custom reports and research on horse health

Feedback

“The system has taken venue management to a completely new level. We receive information about horses in real time, can predict problems, and adapt training. Everything is in one interface, with excellent visualization and in-depth analytics.”

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.