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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
52ba582e54
Adds a community-supported hardware detector for the Qualcomm Hexagon NPU on QCS6490 SoCs (e.g. Radxa Dragon Q6A) via QAIRT 2.37.1 / qai_appbuilder. Mirrors the existing community-board pattern (Rockchip / Synaptics): - frigate/detectors/plugins/qnn.py: detector plugin using yolo-generic model type, lazy SDK import, runs pre-compiled QNN context binaries from Qualcomm AI Hub. - docker/qcs6490/: Dockerfile (two-stage; rebuilds qai_appbuilder wheel inside Frigate's image to match libstdc++ ABI), bake target (qcs6490.hcl), make targets (qcs6490.mk), and a host-side user_installation.sh that installs fastrpc, the QCS6490 firmware (cDSP image + skel libs), and configures cdsprpcd. - .github/workflows/ci.yml: qcs6490_build job mirroring synaptics_build. - CODEOWNERS: /docker/qcs6490/ + qnn.py. - docs: new "Qualcomm Hexagon NPU" sections under Community Supported Detectors in object_detectors.md, plus matching entries in installation.md and hardware.md. Performance on Radxa Dragon Q6A (Hexagon v68, ~12 TOPS), YOLOv8n 640x640: ~10ms per inference under light load, ~24ms with 5 RTSP cameras live. Closes #18602. |
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| frigate | ||
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Frigate NVR™ - Realtime Object Detection for IP Cameras
EnglishA complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras.
Use of a GPU or AI accelerator is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- Tight integration with Home Assistant via a custom component
- Designed to minimize resource use and maximize performance by only looking for objects when and where it is necessary
- Leverages multiprocessing heavily with an emphasis on realtime over processing every frame
- Uses a very low overhead motion detection to determine where to run object detection
- Object detection with TensorFlow runs in separate processes for maximum FPS
- Communicates over MQTT for easy integration into other systems
- Records video with retention settings based on detected objects
- 24/7 recording
- Re-streaming via RTSP to reduce the number of connections to your camera
- WebRTC & MSE support for low-latency live view
Documentation
View the documentation at https://docs.frigate.video
Donations
If you would like to make a donation to support development, please use Github Sponsors.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
Screenshots
Live dashboard
Streamlined review workflow
Multi-camera scrubbing
Built-in mask and zone editor
Translations
We use Weblate to support language translations. Contributions are always welcome.
Copyright © 2026 Frigate, Inc.