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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
01452e4c51
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* show id field when editing zone * improve zone capitalization * Update NPU models and docs * fix mobilepage in tracked object details * Use thread lock for openvino to avoid concurrent requests with JinaV2 * fix hashing function to avoid collisions * remove extra flex div causing overflow * ensure header stays on top of video controls * don't smart capitalize friendly names * Fix incorrect object classification crop * don't display submit to plus if object doesn't have a snapshot * check for snapshot and clip in actions menu * frigate plus submission fix still show frigate+ section if snapshot has already been submitted and run optimistic update, local state was being overridden * Don't fail to show 0% when showing classification * Don't fail on file system error * Improve title and description for review genai * fix overflowing truncated review item description in detail stream * catch events with review items that start after the first timeline entry review items may start later than events within them, so subtract a padding from the start time in the filter so the start of events are not incorrectly filtered out of the list in the detail stream * also pad on review end_time * fix * change order of timeline zoom buttons on mobile * use grid to ensure genai title does not cause overflow * small tweaks * Cleanup --------- 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.
