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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
fbf4388b37
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* don't flatten the search result cache when updating this would cause an infinite swr fetch if something was mutated and then fetch was called again * Properly sort keys for recording summary in StorageMetrics * tracked object description box tweaks * Remove ability to right click on elements inside of face popup * Update reprocess message * don't show object track until video metadata is loaded * fix blue line height calc for in progress events * Use timeline tab by default for notifications but add a query arg for customization * Try and improve notification opening behavior * Reduce review item buffering behavior * ensure logging config is passed to camera capture and tracker processes * ensure on demand recording stops when browser closes * improve active line progress height with resize observer * remove icons and duplicate find similar link in explore context menu * fix for initial broken image when creating trigger from explore * display friendly names for triggers in toasts * lpr and triggers docs updates * remove icons from dropdowns in face and classification * fix comma dangle linter issue * re-add incorrectly removed face library button icons * fix sidebar nav links on < 768px desktop layout * allow text to wrap on mark as reviewed button * match exact pixels * clarify LPR docs --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> |
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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.
