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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
b420efdebd
Adds a per-camera ONVIF subscriber that lets cameras with native
hardware motion detection (e.g. OpenIPC firmware for HiSilicon,
Ingenic and SigmaStar SoCs; many ONVIF Profile-M devices) replace
Frigate's per-frame CPU motion analysis. Two standard ONVIF
transports are consumed in parallel:
- WS-BaseNotification PullPoint for the binary motion state
(tns1:RuleEngine/CellMotionDetector/Motion IsMotion=true|false,
with tns1:VideoSource/MotionAlarm State=true|false accepted as
a fallback for cameras that only publish the legacy topic).
- RTSP analytics metadata stream (application/vnd.onvif.metadata)
for the per-frame cell grid (tt:MotionInCells, base64 + PackBits
bit-packed bitmap). Cell layout is discovered once at startup via
AnalyticsService.GetAnalyticsModules and the camera's CellLayout
transformation is used to map cells to detect-frame pixel
rectangles via connected-components.
New config:
onvif.events.{enabled, subscription_timeout, use_metadata_stream}
motion.source: internal (default) | onvif
When motion.source: onvif, ImprovedMotionDetector is skipped and
motion_boxes come from the camera. Internal motion remains the
default; the new path is fully opt-in.
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| .cursor/rules | ||
| .devcontainer | ||
| .github | ||
| .vscode | ||
| config | ||
| docker | ||
| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| testing-scripts | ||
| web | ||
| .dockerignore | ||
| .gitignore | ||
| .pylintrc | ||
| AGENTS.md | ||
| audio-labelmap.txt | ||
| CLAUDE.md | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
| netlify.toml | ||
| package-lock.json | ||
| pyproject.toml | ||
| README_CN.md | ||
| README.md | ||
| TRADEMARK.md | ||
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.