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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
* remove frigate+ icon from explore grid footer * add margin * pointer cursor on event menu items in detail stream * don't show submit to plus for non-objects and if plus is disabled * tweak spacing in annotation settings popover * Fix deletion of classification images and library * Ensure after creating a class that things are correct * Fix dialog getting stuck * Only show the genai summary popup on mobile when timeline is open * fix audio transcription embedding * spacing * hide x icon on restart sheet to prevent closure issues * prevent x overflow in detail stream on mobile safari * ensure name is valid for search effect trigger * add trigger to detail actions menu * move find similar to actions menu * Use a column layout for MobilePageContent in PlatformAwareSheet This is so the header is outside the scrolling area and the content can grow/scroll independently. This now matches the way it's done in classification * Skip azure execution provider * add optional ref to always scroll to top the more filters in explore was not scrolled to the top on open due to the use of framer motion * fix title classes on desktop --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
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| 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.
