Red5 launches real-time AI frame extraction for live video

Breaking the AI latency barrier

The primary obstacle to using AI with live video has long been the time required to pull individual frames for analysis. While many existing solutions can capture screenshots, they often lag by several seconds, making real-time responsiveness nearly impossible. Red5 is addressing this by introducing a service that extracts frames in milliseconds.

By enabling virtually instant extraction at any frequency down to sub-second intervals, the company aims to unlock new levels of automation. This process works across both conventional HTTP-based streams and high-speed Experience Delivery Network (XDN) infrastructures. The goal is to allow AI to act on video content as it happens rather than as a post-production task.

As reported by red5.net, this technology allows for the analysis of content delivered over a wide variety of protocols. This includes standard formats like RTMP and RTSP, as well as low-latency options like WebRTC and SRT. By standardizing this extraction, the platform creates a foundation for a wide array of intelligent streaming applications.

How the architecture works

The system functions differently depending on the type of stream being used. For conventional HTTP live streaming, such as HLS or DASH, the feed is ingested by the XDN Architecture for frame extraction while the primary content continues to the user. The extracted frames are then pushed to cloud storage, like S3, where AI models can process them instantly.

In some scenarios, the AI processing can actually be completed before the primary content reaches the end user. This is possible when the streaming entity uses low-latency backend connections to the storage repositories. Users can also choose specific settings on the Red5 service portal to automate transcoding and formatting for these frames in real time.

For streamers using end-to-end real-time streaming over XDN, the process is even more direct. In these cases, distributors can apply Large Language Model (LLM) solutions as part of the in-stream flow. This can be done using pre-integrated AI tools or through open APIs, allowing the AI-processed results to be streamed as part of the primary feed or sent separately.

Real-world AI applications

The ability to process frames at this speed opens up several high-impact use cases across different industries. In surveillance, it allows for the immediate detection of specific objects, faces, fires, or criminal activity. It can also be used to filter out unwanted elements like swear words or indecent exposure from live broadcasts.

In the world of sports and news, the technology can automatically locate key moments to generate promotional thumbnails or highlights. Manufacturing environments can use it for quality control by pinpointing defective parts on a production line. Additionally, it can be used for predictive maintenance and improving worker safety in industrial settings.

The service also has significant implications for interactive media and healthcare. For 2D and 3D games, AI can analyze player movements and facial expressions to shape gameplay in real time. In telemedicine, the system can provide real-time AI-assisted diagnostic analysis to complement the observations of healthcare providers during video consultations.

Technical infrastructure and speed

Red5 built its own software transcoder from scratch rather than relying on third-party solutions like FFMPEG. This choice allowed them to shave hundreds of milliseconds off typical latency performance using standard CPUs. Their Cauldron stream processor uses native code modules to handle stream scaling and compression with minimal impact on speed.

The platform operates on the global Oracle Cloud Infrastructure, which spans 50 geographic regions across six continents. It automatically configures resources into clusters based on the number and location of end users. This ensures that the high-speed frame extraction remains consistent regardless of the scale of the audience.

The XDN infrastructure is designed to keep end-to-end latencies imperceptible to humans. For intra-regional distances, these latencies can drop into the 50-ms range. By maintaining these speeds, Red5 ensures that AI-driven content enhancements do not introduce noticeable delays for the viewer.

An AI-agnostic approach

Red5 is taking an AI-agnostic approach to ensure that users have the broadest range of options available. While the company is pre-integrating some specific LLM models, they are also designing the platform to be compatible with industry standards. This means users are free to bring their own AI models into play as long as they use compatible open interfaces.

The company plans to continue assessing and integrating best-of-breed performance options as the technology evolves. By providing open APIs, they allow developers to build custom solutions that can be deployed on the Red5 Cloud. This flexibility is intended to support a wide variety of specialized tasks that may emerge as generative AI continues to advance.

The upcoming commercial launch will include more details on the specific LLMs that will be available at the start. As the service moves toward full availability, the focus remains on providing the infrastructure needed for real-time intelligence. This allows content creators and enterprise users to move from reactive video viewing to proactive, AI-enhanced experiences.