Fluendo and Spyrosoft BSG partner to develop AI-Powered solutions for sports video analysis and broadcast workflows
broadcasting, sports, multimedia-edge-ai, announcementsFluendo and Spyrosoft BSG partner for AI sports video analysis.
Broadcasting
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From the code to the action
Developments that bring real-world results, these case studies show how our solutions help your business achieve goals and enhance user experiences.
The client needed a scalable, modular system capable of handling high volumes of video streams for recording and re-streaming purposes. The solution was required to support live media ingestion, continuous recording, and dynamic re-streaming using standard protocols such as RTSP, HLS, and WebRTC. Additionally, the system had to be API-driven, containerized for flexible deployment, and designed to scale efficiently to support thousands of concurrent channels.
Proposed solutions
Designing modular streaming from the ground up
Scalable, containerized architecture for massive video streaming
We implemented a containerized, Docker-based architecture with built-in support for standard streaming protocols including RTSP, HLS, and WebRTC. The system featured an API-driven configuration layer for dynamic stream management and was architected for scalability, capable of supporting over 10,000 concurrent video channels.
For a closer look at how we approach scalability and re-streaming challenges, check out our article Recasting and recording system, which expands on the techniques and architecture that underpin this project.
The client needed to replace manual and error-prone ad spotting with a high-accuracy AI-powered advertisement detection system that could run privately at the edge. Key requirements included precise ad recognition in both video and audio, support for multiple languages, and offline processing to minimize bandwidth usage, protect sensitive multimedia content, and reduce operational costs.
The solution was delivered as a self-contained Docker application with a full CLI, ensuring portability, easy maintenance, and scalability. Designed for robustness and future-readiness, this AI-driven advertisement detection solution empowers businesses to automate ad tracking while maintaining efficiency, privacy, and cost control.
Proposed solutions
AI at the edge for smarter advertisement detection
From manual spotting to 96% accurate detection
The client transitioned from manual ad spotting to an AI-powered advertisement detection system achieving 96.36% average accuracy with minimal timing offsets across English, Spanish, Basque, Catalan, and Galician. By deploying AI at the edge, the solution eliminates constant cloud dependency, reducing costs, accelerating processing, and ensuring full control of sensitive multimedia content. This not only simplifies compliance but also minimizes manual effort and enables confident verification.
In practice, the system streamlines multimedia operations, strengthens advertiser relationships, and scales seamlessly with demand. Built to support reliable, multilingual ad detection technology, the solution reflects Fluendo’s commitment to delivering future-ready AI solutions for multimedia content analysis.
The company is a technology integrator specialized in complex engineering projects, delivering scalable video infrastructure for government and defense systems. As part of a major public-sector initiative, Fluendo expanded the client’s Recasting & Recording System (RRS) to support real-time video stream ingestion, advanced recasting and recording formats, video transcoding, and asynchronous event notifications. Leveraging a modular GStreamer-based architecture, these enhancements were seamlessly integrated in a short timeframe, ensuring high performance, long-term scalability, and reliability for mission-critical multimedia operations.
Proposed solutions
High-performance RRS implementation
Ensuring reliability and scalability through thorough testing and feature delivery
Fluendo efficiently delivered the requested features, positioning the RRS as a potential replacement for the client’s existing solution. Robust testing ensured high reliability and performance.
The company develops a modern AI-powered camera system designed to create safer workplaces and enable smarter business operations across industries. To strengthen its multimedia engineering capabilities, the company partnered with Fluendo to enhance its team’s expertise in GStreamer with a strong focus on Rust-based development.
Recognizing Rust’s growing adoption for its memory safety and high performance, Fluendo delivered a six-day, hands-on training program combining GStreamer fundamentals with modern Rust practices. Participants were guided through pipeline creation, memory-safe plugin development, interoperability with existing C APIs, and debugging complex multimedia workflows within the Rust ecosystem. The progressive structure of the sessions enabled engineers to directly apply their learnings to the company’s AI video processing products.
Proposed solutions
Empowering Teams with Rust and GStreamer
Building a foundation for safe and high-performance multimedia development
The engineering team gained confidence in integrating Rust into their multimedia stack with GStreamer. They developed a strong foundation in safe, high-performance multimedia development, enabling them to create more robust and efficient multimedia systems internally.
Concepts with real-world potential
These use cases present conceptual examples of how our ideas and technologies could address real-world industry challenges.
As video becomes central to entertainment, social media, e-learning, and news reporting, creators increasingly film in uncontrolled or sensitive environments—city streets, classrooms, hospitals, protests, and corporate settings where bystanders may appear without consent. Under GDPR and similar data-protection laws, publishing identifiable individuals without permission risks legal action and reputational harm. An automated anonymization system can detect and obscure faces in live streams or recordings without undermining creative vision.
Recent advancements in AI and real-time processing now allow scalable anonymization pipelines for post-production, livestreaming, and mobile content capture.
Conceptual Design
How our AI anonymization works: fast, flexible, and built for creators
Anonymizer is a Fluendo AI Plugin built to fit seamlessly into today’s media production workflows, whether editing on a desktop, capturing on mobile, or livestreaming from the field. Powered by a GStreamer pipeline, it delivers real-time face blurring and frame-accurate anonymization across various video formats, codecs, and resolutions.
Designed for content creators, journalists, educators, broadcasters, and corporate teams, Anonymizer enables privacy-safe video creation in both live and post-production scenarios. Its scalable architecture supports on-device and centralized processing, allowing you to automatically anonymize video content anywhere—with minimal setup and no manual intervention—including dedicated features for children anonymization to meet stricter privacy regulations.
Our achievements
On-premise video anonymization for ethical broadcasting in social care
Protect identities without cloud processing. Film in sensitive areas without post-clearance delays — ensuring privacy, compliance, and faster production.
Regulatory compliance
Delivers GDPR-aligned workflows by anonymizing personal data before it is stored or analyzed, ensuring regulatory compliance.
Risk-Proof Creator Protection
Shields against takedowns, lawsuits, and platform penalties.
On-premise video anonymization for ethical broadcasting in social care
Protect identities without cloud processing. Film in sensitive areas without post-clearance delays — ensuring privacy, compliance, and faster production.
Regulatory compliance
Delivers GDPR-aligned workflows by anonymizing personal data before it is stored or analyzed, ensuring regulatory compliance.
Risk-Proof Creator Protection
Shields against takedowns, lawsuits, and platform penalties.
The training provided was incredibly valuable for our team. The new joiners, particularly, found it extremely beneficial as they gained a solid foundation in GStreamer. The experienced team members appreciated discovering new details and enhancing their understanding of GStreamer’s capabilities. While the focus was more on theory, the Rust code examples were also helpful in supporting our transition from C++ to Rust for building GStreamer plugins. Thanks to everyone involved in this!
Bits & Bytes
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Read more about our work
Fluendo x Intelion: Low-latency live video streaming and recording for law enforcement
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Advertisement detection in multimedia content with artificial intelligence
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Real-time 4K face anonymization benchmark (GStreamer plugin)
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