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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
ea131e1663
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Resolve conflicts in the export pipeline where dev's job-queue refactor met master's chapter-metadata and security work. - Unify chapter support under ChaptersEnum (none / recording_segments / review_items); the realtime stream-copy export selects the per-segment or per-review-item builder by the camera's configured mode. Thread chapters through ExportRecordingsBody -> _build_export_job -> ExportJob -> RecordingExporter. - Keep master's creation_time/comment export metadata and fix a video_path duplication the textual merge introduced in the preview command. - Move the chapters request field to ExportRecordingsBody (the single export endpoint) where it is actually honored. Restore security fixes the automatic merge would have reverted: - frigate/util/services.py: restore the #23493 rename to the public is_go2rtc_arbitrary_exec_allowed so create_config.py's dynamic-source exec guard imports and runs (the merge otherwise left a broken import). - Preserve the export image-path ".." traversal check inside _sanitize_existing_image, applied to single/custom/batch exports. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> |
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Frigate NVR™ - 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.
License
This project is licensed under the MIT License.
- Code: The source code, configuration files, and documentation in this repository are available under the MIT License. You are free to use, modify, and distribute the code as long as you include the original copyright notice.
- Trademarks: The "Frigate" name, the "Frigate NVR" brand, and the Frigate logo are trademarks of Frigate, Inc. and are not covered by the MIT License.
Please see our Trademark Policy for details on acceptable use of our brand assets.
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.
Copyright © 2026 Frigate, Inc.