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# GStreamer 1.24: New heights in embedded, network streaming & analytics

**[Collabora Blog](https://daily.dev/sources/collabora)** · 8 min read · 0 upvotes · 0 comments

## Summary

GStreamer 1.24 has been released after 13 months of development, with contributions from 231 developers and over 3,141 patches. Key highlights include a new W3C Media Source Extensions (MSE) library enabling adaptive streaming without a browser engine, a LiveKit SFU signaller written in Rust for simplified WebRTC use-cases, DRM Dumb allocator improvements for software rendering on GPU-less processors, and a new unixfd plugin enabling zero-copy inter-process buffer transfer (sponsored by Netflix). On the analytics front, the release introduces a machine learning foundation: OnnxInference has been refactored into a generic element compatible with any ONNX model, and a new Analytics-Meta transport mechanism supports multi-level, multi-modal analytics pipelines. V4L2 gains new pixel formats, MediaTek tiling support, and an AV1 stateless decoder. The gst-omx plugin has been retired, and CI improvements now allow v4l2 codec testing in cloud environments using QEMU.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.collabora.com/news-and-blog/news-and-events/gstreamer-124-new-heights-embedded-network-streaming-analytics.html>

## Questions this post answers

### What is the new Analytics-Meta feature in GStreamer 1.24 and what problem does it solve?

Analytics-Meta is a transport mechanism introduced in GStreamer 1.24 for carrying analytics results through pipelines. It is memory-efficient and designed to describe relationships between analytics results, supporting heterogeneous pipelines that combine computer vision and ML, multi-level scenarios with multiple ML models, and generic result consumption such as analytics overlay and Onvif serialization. It relies on a core GStreamer change allowing metadata re-use.

_Engineers building video analytics pipelines track GStreamer analytics developments on daily.dev._

### What changed with OnnxInference in GStreamer 1.24 compared to the previous onnxobjectdetector?

OnnxInference in GStreamer 1.24 is a major refactor of the former onnxobjectdetector, now made generic and compatible with any ONNX model rather than being tied to object detection. Inference and tensor-decoding are separated: onnxinference handles inference generically, while model-specific decoding is handled by independent elements like ssdobjectdetector. It supports multiple backends via ONNX Runtime for improved performance.

_Developers integrating ML inference into GStreamer pipelines follow ONNX and GStreamer updates on daily.dev._

### How does the new unixfd plugin in GStreamer 1.24 enable zero-copy inter-process pipelines?

The unixfd plugin provides sink and source elements that communicate over a Unix socket, enabling zero-copy transfer of file-descriptor-backed buffers between pipelines running in separate processes. It is complemented by a new generic GstMeta serialization API that allows metadata to be transmitted and stored alongside buffers. This work was sponsored by Netflix.

_Teams building high-performance GStreamer pipelines across processes can follow related releases on daily.dev._

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---

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