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NVR with realtime local object detection for IP cameras

ai camera google-coral home-assistant home-automation homeautomation mqtt nvr object-detection realtime rtsp tensorflow

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.vscode	Fix vscode launch configuration (#13795 )	2024-09-17 10:42:10 -05:00
config	Improve the devcontainer experience (#3492 )	2022-11-20 07:34:12 -06:00
docker	update legacy intel-compute packages (#19848 )	2025-08-31 06:20:50 -06:00
docs	Fix typo in Apple Silicon detector (#19854 )	2025-08-31 22:10:52 -05:00
frigate	Replace hardcoded path with constant (#19816 )	2025-09-01 07:26:04 -06:00
migrations	Semantic Search Triggers (#18969 )	2025-08-16 10:20:33 -05:00
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.pylintrc	use fstr log style	2021-02-25 07:01:59 -06:00
audio-labelmap.txt	Audio events (#6848 )	2023-07-01 08:18:33 -05:00
benchmark_motion.py	use a different method for blur and contrast to reduce CPU (#6940 )	2023-06-30 07:27:31 -05:00
benchmark.py	Fix go2rtc init (#18708 )	2025-08-16 10:20:33 -05:00
CODEOWNERS	Initial support for Hailo-8L (#12431 )	2024-08-29 20:19:50 -06:00
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docker-compose.yml	Devcontainer: update Mosquitto from 1.6 to 2.0 (#17415 )	2025-03-27 10:33:49 -06:00
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netlify.toml	Docs improvements (#8641 )	2023-11-18 08:04:43 -06:00
package-lock.json	Implement support for notifications (#12523 )	2024-08-29 20:19:50 -06:00
process_clip.py	Improve async object detector support (#17712 )	2025-04-15 08:55:38 -05:00
pyproject.toml	Fix various typing issues (#18187 )	2025-05-13 08:27:20 -06:00
README_CN.md	Add Chinese community sponsors (#18945 )	2025-07-04 14:32:48 -05:00
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README.md

Frigate - NVR With Realtime Object Detection for IP Cameras

English

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.