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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
* semantic trigger test * database and model * config * embeddings maintainer and trigger post-processor * api to create, edit, delete triggers * frontend and i18n keys * use thumbnail and description for trigger types * image picker tweaks * initial sync * thumbnail file management * clean up logs and use saved thumbnail on frontend * publish mqtt messages * webpush changes to enable trigger notifications * add enabled switch * add triggers from explore * renaming and deletion fixes * fix typing * UI updates and add last triggering event time and link * log exception instead of return in endpoint * highlight entry in UI when triggered * save and delete thumbnails directly * remove alert action for now and add descriptions * tweaks * clean up * fix types * docs * docs tweaks * docs * reuse enum |
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frigate | ||
migrations | ||
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web | ||
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audio-labelmap.txt | ||
benchmark_motion.py | ||
benchmark.py | ||
CODEOWNERS | ||
cspell.json | ||
docker-compose.yml | ||
labelmap.txt | ||
LICENSE | ||
Makefile | ||
netlify.toml | ||
package-lock.json | ||
process_clip.py | ||
pyproject.toml | ||
README_CN.md | ||
README.md |
Frigate - NVR With Realtime Object Detection for IP Cameras
English
A 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 such as a Google Coral or Hailo is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
- 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.
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.