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Josh HawkinsandGitHub
ff9eb8f184
* keep frame shm open and skip overwritten frames
Every reader closed and reopened a frame's shared memory segment on each frame, so each process paid a page fault on every page of a fresh mapping (about 760 for a 1080p frame) per frame. Readers now keep segments mapped. Capture stamps each slot's frame time in a per-camera `{camera}_frame_times` segment, and `get()` returns None when the slot no longer holds the requested frame, reopening once by name so a mapping left over from a same-size camera recreate heals itself. Capture also reuses a slot when the frame queue is full, since no reader was told about it.
* verify the frame for embeddings object updates
Tracked object events only carried the object's own frame_time, which stays at its last detection when the object isn't detected in a frame, so embeddings couldn't check the slot it read and could crop face or plate data from a frame that had already been overwritten. Events now carry the time of the frame they name, and embeddings passes it to `get()`.
* log when a frame slot was overwritten before it was read
A lapped read and a missing segment ended up as the same debug line at every caller, so there was no way to tell a slow reader from a missing frame.
* bring the FrameManager interface in line with its implementation
The abstract get() still had a timeout_ms signature from an older implementation and frame_times() wasn't declared, though capture and detect are typed against the interface. get() also didn't say how long the array it returns stays valid.
* drop a camera's cached frame mappings when it is removed or recreated
A reader that kept its mappings open stayed on a removed camera's segments, which pinned their shared memory until restart, and after a same-size recreate the ledger reopen only refreshed the slot being read while the other cached slots still returned the old camera's pixels. Readers now drop every cached slot for a camera on its remove update, and get() does the same when a ledger mismatch turns out to be a replaced ledger rather than a lapped slot.
* match a camera's frame names exactly in close_camera
The prefix match on `{camera}_frame` also caught another camera whose name starts with it, so removing `cam` dropped the cached ring of a camera named `cam_frame`.
Frigate NVR™ - Realtime Object Detection for IP Cameras
[English] | 简体中文
A 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.