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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
9d85136f8f
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* fetch more from ffprobe * add detailed param to ffprobe endpoint * add dots variant to step indicator * add classname * tweak colors for dark mode to match figma * add step 1 form * add helper function for ffmpeg snapshot * add go2rtc stream add and ffprobe snapshot endpoints * add camera image and stream details on successful test * step 1 tweaks * step 2 and i18n * types * step 1 and 2 tweaks * add wizard to camera settings view * add data unit i18n keys * restream tweak * fix type * implement rough idea for step 3 * add api endpoint to delete stream from go2rtc * add main wizard dialog component * extract logic for friendly_name and use in wizard * add i18n and popover for brand url * add camera name to top * consolidate validation logic * prevent dialog from closing when clicking outside * center camera name on mobile * add help/docs link popovers * keep spaces in friendly name * add stream details to overlay like stats in liveplayer * add validation results pane to step 3 * ensure test is invalidated if stream is changed * only display validation results and enable save button if all streams have been tested * tweaks * normalize camera name to lower case and improve hash generation * move wizard to subfolder * tweaks * match look of camera edit form to wizard * move wizard and edit form to its own component * move enabled/disabled switch to management section * clean up * fixes * fix mobile |
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Frigate - NVR With 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 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.
