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* perf(util): use monotonic clock and bounded deque in EventsPerSecond EventsPerSecond is updated on every captured frame, every detection and every processed frame across all cameras and detectors. The previous implementation derived timestamps from datetime.now().timestamp() (wall clock), so an NTP or manual clock adjustment could skew the rolling-window expiry; it also stored timestamps in a list and expired them with del self._timestamps[0] (O(n) per removal) plus a periodic slice-copy to cap growth. Switch to time.monotonic() for the interval math (correct by construction and immune to wall-clock jumps) and a collections.deque(maxlen=...) so expiry is O(1) (popleft) and retention is bounded automatically. This mirrors the deque-based expiry already used in video/ffmpeg.py and watchdog.py. Observable output is unchanged. Adds frigate/test/test_builtin.py covering rate calculation, window expiry and the memory bound. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> * test: drop test_timestamps_are_memory_bounded It only asserted that deque(maxlen=) caps length, which is stdlib behavior rather than something this change needs to verify. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> --------- Co-authored-by: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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.