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@@ -15,7 +15,7 @@ In most cases, pipelines are used in one of the following scenarios:
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This approach is ideal when your pipeline architecture is fixed and needs to run continuously from the moment your application starts.
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Imagine an application that monitors a security camera feed to detect people or vehicles in real-time. Such an application could consist of two components:
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- The Membrane Pipeline which connects to an SRT stream, decodes the video, and extracts raw frames. It sends these frames to the external process (via a Unix socket or standard input), receives the transformed video back, re-encodes it, and broadcasts the final stream using HLS.
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- The Membrane Pipeline which connects to an RTSP stream, decodes the video, and extracts raw frames. It sends these frames to the external process (via a Unix socket or standard input), receives the transformed video back, re-encodes it, and broadcasts the final stream using HLS.
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- The OS Process being a Python script running a machine learning model like [RF-DETR](https://github.com/roboflow/rf-detr). It reads raw video frames and performs object segmentation, coloring the pixels that correspond to detected objects.
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