mirror of
https://github.com/blakeblackshear/frigate.git
synced 2026-11-12 00:23:01 +03:00
Josh HawkinsandGitHub
8a88cefc9a
* fix autotracking movement time prediction and recorded move times Movement time prediction multiplied the fitted intercept by the saved zero-step calibration time instead of 1, so predictions didn't match the regression. In absolute zoom mode, a pan/tilt followed by a zoom recorded the zoom's timestamps against the pan/tilt distance, because _zoom_absolute resets the PTZ start and stop times. The pan/tilt times are now captured before the zoom runs. * predict velocity and zoom size for objects approaching the autotracked camera The velocity check required both box corners to move in the same direction, but the corners of an approaching object diverge, so its velocity was rejected and zeroed and the camera aimed where the object had been. That check is removed, and the existing magnitude, delta, and variance checks still reject noise. The zoom size prediction fit area against epoch time with no intercept, which returned roughly the mean area instead of a forecast. It now scales the target box by the growth the corner velocities predict over the movement time. * keep predicted target box positive and add regression tests A shrinking object's predicted size could reach zero, so the relative zoom ratio divided by zero and sent a NaN zoom to the camera. The predicted size is now floored at 1 px. Tests also cover the movement time intercept and the pan/tilt timestamps that absolute zoom overwrites.
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.