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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
4d4d54d030
* Fix the `Any` typing hint treewide There has been confusion between the Any type[1] and the any function[2] in typing hints. [1] https://docs.python.org/3/library/typing.html#typing.Any [2] https://docs.python.org/3/library/functions.html#any * Fix typing for various frame_shape members Frame shapes are most likely defined by height and width, so a single int cannot express that. * Wrap gpu stats functions in Optional[] These can return `None`, so they need to be `Type | None`, which is what `Optional` expresses very nicely. * Fix return type in get_latest_segment_datetime Returns a datetime object, not an integer. * Make the return type of FrameManager.write optional This is necessary since the SharedMemoryFrameManager.write function can return None. * Fix total_seconds() return type in get_tz_modifiers The function returns a float, not an int. https://docs.python.org/3/library/datetime.html#datetime.timedelta.total_seconds * Account for floating point results in to_relative_box Because the function uses division the return types may either be int or float. * Resolve ruff deprecation warning The config has been split into formatter and linter, and the global options are deprecated. |
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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.
