RidgeRun Software Developer Blog

Real-time AI at the edge is reshaping industries from medical devices to broadcast media. NVIDIA’s ecosystem—GStreamer, DeepStream, Holoscan SDK, and Holoscan for Media—offers multiple approaches to building streaming AI pipelines, each with distinct trade-offs in latency, scalability, and architecture. This deep dive clarifies how these frameworks differ and when to use each.

See how to start working with the Prophesee GENX320 event camera on Raspberry Pi 5 and Jetson Orin NX, including RidgeRun’s open-source Jetson driver and benchmark results.

Event-based cameras offer a different approach to machine vision by transmitting only changes in brightness. Learn where they fit best, what trade-offs to expect, and how RidgeRun helps clients with driver development.

The NXP i.MX95 platform is designed to meet the growing demands of edge AI and modern embedded systems, combining heterogeneous processing, a dedicated Neural Processing Unit (NPU), and advanced multimedia capabilities. This article provides a decision-focused overview of its architecture, software ecosystem, and key use cases, helping technical leaders evaluate its suitability for industrial, automotive, and intelligent IoT applications.

Media over QUIC (MoQ) enables low-latency, multi-track streaming for robotics, edge AI, and embedded systems. Built on QUIC, it supports secure, multiplexed data delivery with no head-of-line blocking. RidgeRun’s Gst-MoQ solution demonstrates scalable publisher–relay–subscriber architecture, dynamic track selection, synchronized telemetry, and real-time multi-camera streaming.

RidgeRun Video Stabilization Library now makes real-time IMU-based video stabilization easier to evaluate on embedded systems with guided calibration, timestamp offset estimation, tuning workflows, and objective validation metrics.

Modern video workflows demand more than visuals. They require context: timestamps aligned to frames, geolocation and telemetry for situational awareness, and AI insights such as bounding boxes and labels that stay perfectly synchronized through encode, transport, and playback. RidgeRun’s Metadata Suite delivers exactly that end-to-end, in-band metadata across codecs and transports, designed for production reliability without adding complexity. What You’ll Get from This Post A

Live streaming today isn’t only about video and audio. Applications in AI, robotics, and broadcasting demand contextual information - detections, telemetry, captions, and more - that travels alongside the stream. While RTMP reliably carries audio, video, and standard metadata into platforms like YouTube , Twitch , and Facebook Live , it wasn’t designed to handle custom, application-specific metadata . Developers who need to send analytics, sensor data, or AI events typicall

Gain full visibility into your GStreamer pipelines with RidgeRun’s Analytics Tool. Monitor system performance, track pipeline metrics, and debug issues in real time using powerful dashboards and centralized logging.