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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
224cbdc2d6
* Include DB in safe mode config Copy DB when going into safe mode to avoid creating a new one if a user has configured a separate location * Fix documentation for example log module * Set minimum duration for recording segments Due to the inpoint logic, some recordings would get clipped on the end of the segment with a non-zero duration but not enough duration to include a frame. 100 ms is a safe value for any video that is 10fps or higher to have a frame * Add docs to explain object assignment for classification * Add warning for Intel GPU stats bug Add warning with explanation on GPU stats page when all Intel GPU values are 0 * Update docs with creation instructions * reset loading state when moving through events in tracking details * disable pip on preview players * Improve HLS handling for startPosition The startPosition was incorrectly calculated assuming continuous recordings, when it needs to consider only some segments exist. This extracts that logic to a utility so all can use it. --------- Co-authored-by: Josh Hawkins <32435876+hawkeye217@users.noreply.github.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 is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead. See Frigate's supported object detectors.
- 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.
