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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
56a128b776
## Proposed change Add websockets transport mechanism to MQTT. An MQTT server is not always reachable only as raw TCP on port 1881; it can also be accessed as a WebSocket server, sometimes even behind a reverse proxy with TLS. Paho MQTT supports this feature, as does Mosquitto. The default transport option was set to 'tcp', so this change will not affect an already configured Frigate app. The configuration change is validated using a call to a pydantic library field validator. This patch has been tested with Mosquitto 1.6 and 2.0, as well as with a server behind a reverse proxy on port 443 (requiring a TLS connection). The docs/configuration/reference.md file has also been updated to reflect this change. ## Type of change - [ ] Dependency upgrade - [ ] Bugfix (non-breaking change which fixes an issue) - [x] New feature - [ ] Breaking change (fix/feature causing existing functionality to break) - [ ] Code quality improvements to existing code - [x] Documentation Update ## Additional information - This PR fixes or closes issue: fixes #15600 - This PR is related to issue: ## Checklist - [x] The code change is tested and works locally. - [ ] Local tests pass. **Your PR cannot be merged unless tests pass** - [x] There is no commented out code in this PR. - [x] The code has been formatted using Ruff (`ruff format frigate`) |
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Frigate - NVR With Realtime Object Detection for IP Cameras
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 Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS 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.