AI-Powered Object Detection System for Sports Score Recognition
- AI Development
- Computer Vision
- LangChain
- Sports Betting
- Real-Time Processing
Overview of Our Client
Our client operated in the sports analytics domain, providing live statistics and insight updates about games as they happened.
It was very important for their service to have accurate and fast score tracking. However, the workflows in place were very dependent on human input, which not only caused delays but also led to mistakes.
Given these circumstances, the client needed an automated solution capable of detecting scores directly from live video streams.
- Region: Global
- Industry: Sports Analytics / Media
- Timeline: ~2 months
Challenge
Live sports data processing is one of those areas where it is important not only to be fast but also to be very accurate and dependable. Given this fact, our major challenges were:
- Manual score tracking that caused delays and was prone to errors
- The difficulty of recognizing scores within extremely dynamic or blurred segments of video streams
- Scoreboards that were very different from one sport to another, and even from one broadcast to another
- Requirement of minimal latency for live processing
- Working together with traditional statistics and notification systems
Main Goals
We set the following goals to address the challenges described above:
- Automate score detection from live video data
- Recognize the types and locations of objects (scoreboards, digits, overlays)
- Support real-time or near real-time processing
- Enhance accuracy compared to manual data entry methods
- Allow easy integration with analytics and notification services
Project Overview
We developed a computer vision system based on YOLO NAS to detect and recognize objects in live sports broadcasts. The model analyzed the video frame, located the scoreboard area within the frame, and extracted numeric values from the scoreboard.
We utilized LangChain to coordinate and process detection results, facilitating their transfer to subsequent workflows. The model was designed to be universal across all sports, providing support for variations in scoreboard layouts and formats.
Solution
We created a solution that employed an AI-based object detection pipeline to automatically recognize scores and extract data from live sports broadcasts. The system received incoming frames, recognized the relevant objects, and turned visual information into structured data to be used in real-time.
Core Platform Capabilities
- Scoreboard detection and object positioning within video frames
- Identification of scores, timers, and other important information
- Real-time processing and data extraction pipeline
- Compatibility with various sports and broadcasting formats
- Integration with statistical tools and alert systems
- Minimization of manual entry and thus errors
- Validation tests to make sure accuracy thresholds are achieved
Technology Stack
To enable real-time object detection and scores, we utilized an architecture centered on computer vision and augmented with AI-driven workflow orchestration capabilities.
Backend
- Python-based processing services
Computer Vision
- YOLO NAS model (object detection and localization)
AI Orchestration
- LangChain (processing pipelines and integration logic)
Data Processing
- Video frame analysis and structured data extraction
Related Cases
- Node.js
- React
- AWS
- Java
- Hadoop
- Kafka
- Web3
- Node.js
- React
Core Team
- Solution Architect: Built a computer vision pipeline and system architecture
- AI Engineers: Implemented YOLO NAS model and detection logic
- Backend Engineers: Built processing services and integrations
- QA Engineers: Validated detection precision and live performance
Results
Our AI-driven solution tangibly improved the processing of sports data. Specifically, we managed to achieve the following results:
- Automatic score detection with little human intervention
- Improved real-time statistics
- Higher precision compared to human-based tracking
- Scalability across different formats of sports
- Data integration into analytical and notification systems