We engineered an AI-powered system capable of detecting penalty events in football matches by combining computer vision models with temporal sequence learning.
Unreliable event detection systems
High operational cost due to manual verification
Inability to use real-time analytics for live match broadcasts
Low accuracy because existing models lacked contextual understanding
Frame Ingestion using AWS Media Services
Object Detection (YOLO) to track ball and goalpost
Feature Extraction (VGG16 CNN) to generate spatial features
Temporal Modeling (LSTM) to learn context and classify penalty vs. non-penalty
Post-Processing with Heuristics + GenAI Analysis to eliminate false positives
Output Visualizationthrough Grafana-based dashboards
Our solution ensures a significant reduction in false penalty detections, enhancing the accuracy of automated sports analytics. With real-time classification designed to meet broadcast-level requirements, it delivers instant insights during live events. The highly scalable architecture supports continuous streaming workloads, providing consistent performance and improved reliability for teams, broadcasters, and fans alike.
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