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NVR with realtime local object detection for IP cameras
aicameragoogle-coralhome-assistanthome-automationhomeautomationmqttnvrobject-detectionrealtimertsptensorflow
952dc1c3ef
io.BufferedReader.read(n) is documented to return "up to" n bytes, and
a subprocess pipe will return a short read whenever the producer closes
the pipe mid-frame — even on a blocking pipe. The existing assignment
frame_buffer[:] = ffmpeg_process.stdout.read(frame_size)
then raises ValueError on the slice assignment, and the surrounding
``except`` only breaks out of the capture loop when ``poll()`` reports
the process has already exited. If ffmpeg is still alive (e.g. an RTSP
source that dropped a chunk and recovered), the loop hits ``continue``
and the next read picks up data from the middle of the next frame,
putting capture out of sync with frame boundaries until the watchdog
later forces a restart.
Read into the pre-allocated buffer in a loop and treat any short read
as the end of the stream, so the watchdog can respawn ffmpeg from a
clean state rather than continuing at the wrong offset. As a side
effect, ``readinto`` writes directly into the shared-memory frame
buffer, avoiding the per-frame Python ``bytes`` allocation and copy
that the previous slice assignment performed.
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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.