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NVR with realtime local object detection for IP cameras
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Blake Blackshear d81dd60fef
Improve motion detection and region selection ()
* refactor existing motion detector

* implement and use cnt bgsub

* pass fps to motion detector

* create a simplified motion detector

* lightning detection

* update default motion config

* lint imports

* use estimated boxes for regions

* use improved motion detector

* update test

* use a different strategy for clustering motion and object boxes

* increase alpha during calibration

* simplify object consolidation

* add some reasonable constraints to the estimated box

* adjust cluster boundary to 10%

* refactor

* add disabled debug code

* fix variable scope
2023-06-11 08:45:11 -05:00
.devcontainer Update deps () 2023-06-01 06:59:50 -05:00
.github Bump docker/login-action from 2.1.0 to 2.2.0 () 2023-06-11 07:21:59 -05:00
.vscode Set User Agent for FFmpeg calls () 2022-11-30 16:53:45 -06:00
config Improve the devcontainer experience () 2022-11-20 07:34:12 -06:00
docker Add API and WebUI to export recordings () 2023-06-08 07:32:35 -04:00
docs Improve motion detection and region selection () 2023-06-11 08:45:11 -05:00
frigate Improve motion detection and region selection () 2023-06-11 08:45:11 -05:00
migrations DB Optimizations () 2023-06-11 07:23:18 -05:00
web Option to enable / disable stats that require external processes () 2023-06-11 08:26:34 -05:00
.dockerignore Improve the devcontainer experience () 2022-11-20 07:34:12 -06:00
.gitignore Migrate default database path from /media/frigate to /config () 2023-04-23 11:35:40 -05:00
.pylintrc use fstr log style 2021-02-25 07:01:59 -06:00
benchmark_motion.py Improve motion detection and region selection () 2023-06-11 08:45:11 -05:00
benchmark.py Add isort and ruff linter () 2023-05-29 05:31:17 -05:00
docker-compose.yml Migrate default database path from /media/frigate to /config () 2023-04-23 11:35:40 -05:00
Dockerfile fix: don't assume build platform is amd64 () 2023-06-05 16:13:54 -05:00
labelmap.txt Cleanup Detector labelmap () 2023-01-06 07:03:16 -06:00
LICENSE switch to MIT license 2020-07-26 12:07:47 -05:00
Makefile Migrate default database path from /media/frigate to /config () 2023-04-23 11:35:40 -05:00
process_clip.py Improve tracking () 2023-05-31 08:12:43 -06:00
pyproject.toml Fix bug introduced in new linter () 2023-06-11 07:18:47 -05:00
README.md Clarify docs about rtmp () 2023-01-13 07:20:25 -06:00
requirements-dev.txt Add isort and ruff linter () 2023-05-29 05:31:17 -05:00
requirements-ov.txt update python deps () 2023-04-16 07:20:51 -05:00
requirements-tensorrt.txt Update library loading for tensorrt () 2023-01-14 13:14:27 -06:00
requirements-wheels.txt Update deps () 2023-06-01 06:59:50 -05:00
requirements.txt dependency updates () 2023-04-26 06:20:29 -05:00

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Frigate - NVR With Realtime Object Detection for IP Cameras

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 Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS 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

Integration into Home Assistant

Also comes with a builtin UI:

Events