Building AI Camera Video Pipelines with GStreamer

GStreamer Basics

GStreamer is a multimedia processing pipeline framework. In AI camera development, it can handle everything from video input to AI inference, encoding, and streaming within a single pipeline.

A Typical AI Camera Pipeline

gst-launch-1.0 \
  v4l2src device=/dev/video0 ! \
  videoconvert ! \
  tee name=t ! \
  queue ! videoscale ! \
  tensor_converter ! \
  tensor_filter framework=tensorflow-lite model=yolov5s.tflite ! \
  tensor_decoder mode=bounding_boxes ! \
  videoconvert ! x264enc ! \
  filesink location=output.mp4 \
  t. ! queue ! x264enc tune=zerolatency ! \
  rtph264pay ! webrtcbin

Pipeline Design Considerations

1. Use the tee element to split video streams, running AI inference, recording, and streaming in parallel
2. Use queue elements to buffer and absorb processing speed differences between branches
3. Leverage hardware encoders (H.264/H.265)

Summary

By designing GStreamer pipelines properly, you can build a feature-rich AI camera system while keeping CPU load low.