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* Add optional idle heartbeat for Birdseye (periodic frame emission when idle) birdseye: add optional idle heartbeat and FFmpeg tuning envs (default off) This adds an optional configuration field `birdseye.idle_heartbeat_fps` to enable a lightweight idle heartbeat mechanism in Birdseye. When set to a value greater than 0, Birdseye periodically re-sends the last composed frame during idle periods (no motion or active updates). This helps downstream consumers such as go2rtc, Alexa, or Scrypted to attach faster and maintain a low-latency RTSP stream when the system is idle. Key details: - Config-based (`birdseye.idle_heartbeat_fps`), default `0` (disabled). - Uses existing Birdseye rendering pipeline; minimal performance impact. - Does not alter behavior when unset. Documentation: added tip section in docs/configuration/restream.md. * Update docs/docs/configuration/restream.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Update docs/docs/configuration/reference.md Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> * Refactors Birdseye idle frame broadcasting Simplifies the idle frame broadcasting logic by removing the dedicated thread. The idle frame is now resent directly within the main loop, improving efficiency and reducing complexity. Also, limits the idle heartbeat FPS to a maximum of 10 since the framebuffer is limited to 10 anyway * ruff fix --------- Co-authored-by: Nicolas Mowen <nickmowen213@gmail.com> Co-authored-by: Francesco Durighetto <francesco.durighetto@subbyx.com> Co-authored-by: duri <duri@homelabubuntu.durihome.unifi>
Frigate - NVR With 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 such as a Google Coral or Hailo is highly recommended. AI accelerators will outperform even the best CPUs with very little overhead.
- 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.
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.
