Sports video analysis software & services

How AI anonymizes individuals in youth and academy sports recordings

Our solution uses deep learning models to detect and segment individuals appearing in sports video recordings. This includes not only players on the field, but also coaches, referees, staff members, and spectators who may appear in the footage. Once detected, the system applies privacy-preserving transformations such as face blurring, pixelation, or full-body masking to ensure that individuals cannot be identified.

Video streams are processed through an AI pipeline integrated directly into the sports video workflow. The system analyzes each frame to identify people present in the scene and automatically applies anonymization techniques while preserving the overall visual context of the match or training session.

In addition to anonymization, the system can extract structured metadata describing the scene, such as the presence and positioning of individuals on the field or in surrounding areas. This allows sports organizations to maintain useful analytical capabilities for coaching review, performance analysis, or content management, while ensuring that the identities of players, staff, and spectators remain protected.

By combining automated anonymization with metadata generation, sports organizations can safely store, analyze, and share youth and academy sports recordings without exposing personal identities.

How LLM-powered semantic search explores sports archives

Our solution uses Large Language Models (LLMs) combined with semantic vector databases to enable advanced exploration of sports media and document archives. Video transcripts, match reports, scouting notes, and editorial documents are processed using AI models that extract meaningful textual representations and convert them into semantic embeddings.

These embeddings are stored in a vector database that allows users to perform natural language queries instead of relying on rigid keyword searches. The system identifies semantically related content across large archives and retrieves relevant documents, clips, or reports even when exact keywords are not present.

On top of the semantic retrieval layer, LLMs can analyze the retrieved information and generate structured outputs such as summaries, contextual explanations, or draft editorial content. This allows journalists, analysts, and club staff to quickly explore historical information, understand trends, and produce content based on large volumes of archived material.

By integrating semantic search with modern multimedia infrastructures, sports organizations can transform fragmented document collections into navigable knowledge bases, enabling faster research, improved storytelling, and more efficient use of historical sports data.

How AI extracts sports metrics and embeds them into video streams

Our solution applies deep learning models to analyze sports video and extract structured information about players and their movements on the field. The AI pipeline processes each frame to identify athletes, estimate their spatial positions, and derive time-varying motion-related metrics.

From this visual analysis, the system can generate a variety of performance indicators, including player positioning, distance travelled, movement intensity, speed estimation, and spatial heatmaps. These metrics provide valuable insights for performance analysis, coaching review, and tactical evaluation.

In addition to player detection, the system can also generate precise segmentation masks of athletes, enabling advanced visual effects and broadcast graphics. These masks allow production teams to create layered overlays such as tactical visualizations, sponsor graphics, or dynamic highlights while maintaining a clear separation between players and the background scene.

All generated insights can be packaged as structured metadata and embedded directly into the video stream using multimedia container formats and metadata channels supported by the video pipeline. By integrating this process into a GStreamer-based workflow, the system enables AI-generated sports analytics to travel alongside the video content itself.

This approach allows downstream systems—including broadcast graphics engines, analytics dashboards, and archive platforms—to access performance data directly from the video stream, simplifying integration and enabling real-time data-driven sports productions.

How AI anonymizes individuals in sports live broadcasts

Our solution uses deep learning models to detect individuals appearing in live video streams, including players, staff members, spectators, and children present in the background during interviews, commentary segments, or live coverage.

Each video frame is analyzed in real time to identify visible faces or people in the scene. Once detected, the system automatically applies privacy-preserving transformations such as face blurring, pixelation, or masking, ensuring that individuals cannot be identified while preserving the visual integrity of the broadcast.

The system is designed to operate within high-performance live production environments, supporting video resolutions up to 4K and 8K, and high frame rates including 60 fps and 120 fps. Thanks to an ultra-optimized multimedia AI pipeline, anonymization can be performed with very low latency, ensuring that the broadcast workflow remains uninterrupted.

The solution integrates directly into professional streaming and broadcast pipelines, enabling anonymization to occur before encoding or distribution to streaming platforms. Processing can be deployed on edge AI devices located in stadiums or production units, or in cloud-based infrastructures, depending on the production workflow.

By combining optimized AI inference with high-performance video processing, sports organizations can safely produce interviews, analyst shows, live streams, and behind-the-scenes content while protecting the identities of children, spectators, and staff—even in high-resolution, high-frame-rate live productions running on compact edge hardware.