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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
3b554824a6
Currently translated at 1.6% (1 of 62 strings) Translated using Weblate (Danish) Currently translated at 100.0% (183 of 183 strings) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Added translation using Weblate (Danish) Co-authored-by: Hosted Weblate <hosted@weblate.org> Co-authored-by: Rene-Olskjaer <rene@olskjaer.dk> Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/common/da/ Translate-URL: https://hosted.weblate.org/projects/frigate-nvr/views-facelibrary/da/ Translation: Frigate NVR/common Translation: Frigate NVR/views-facelibrary |
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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.
