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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
4772e6a2ab
* tweak language * show validation errors in json response * fix export hwaccel args field in UI * increase annotation offset consts * fix save button race conditions, add reset spinner, and fix enrichments profile leak - Disable both Save and SaveAll buttons while either operation is in progress so users cannot trigger concurrent saves - Show activity indicator on Reset to Default/Global button during the API call - Enrichments panes (semantic search, genai, face recognition) now always show base config fields regardless of profile selection in the header dropdown * fix genai additional_concerns validation error with textarea array widget The additional_concerns field is list[str] in the backend but was using the textarea widget which produces a string value, causing validation errors. Created a TextareaArrayWidget that converts between array (one item per line) and textarea display, and switched additional_concerns to use it * populate and sort global audio filters for all audio labels * add column labels in profiles view * enforce a minimum value of 2 for min_initialized * reuse widget and refactor for multiline * fix * change record copy preset to transcode audio to aac |
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| 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.