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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
317d1acfe1
pandas 3.0 changed DatetimeIndex internal storage from datetime64[ns]
(nanoseconds) to datetime64[us] (microseconds). The motion activity
endpoint in review.py converted DatetimeIndex to epoch seconds using:
df.index = df.index.astype(int) // (10**9)
This assumed nanosecond resolution, dividing by 10^9 to get seconds.
With microsecond resolution the division produces values ~1000x too
small (e.g. 1774785 instead of 1774785600), causing every entry to
have a start_time near zero. The frontend timeline could not match
these timestamps to the visible range, so motion indicator bars
disappeared entirely — despite the underlying recording data being
correct.
Replace the resolution-dependent integer division with pandas
Timedelta arithmetic:
df.index = (df.index - _EPOCH) // _ONE_SECOND
This is resolution-independent (produces correct results on
datetime64[s], [ms], [us], and [ns]), ~148x faster than the
per-element .timestamp() alternative, produces native Python int
types that serialize cleanly to JSON, and is backwards-compatible
with older pandas versions.
Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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| docs | ||
| frigate | ||
| migrations | ||
| notebooks | ||
| web | ||
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| audio-labelmap.txt | ||
| benchmark_motion.py | ||
| benchmark.py | ||
| CODEOWNERS | ||
| CONTRIBUTING.md | ||
| cspell.json | ||
| docker-compose.yml | ||
| generate_config_translations.py | ||
| labelmap.txt | ||
| LICENSE | ||
| Makefile | ||
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| package-lock.json | ||
| process_clip.py | ||
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| README_CN.md | ||
| README.md | ||
| TRADEMARK.md | ||
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.