i only have 1 camera with Frigate. my HA with Frigate add-on is on an i3 laptop with 8gb ram, with the NUC hassOS image.
any idea why my sensor.cpu sometimes hit 100%?
this is my Frigate.yml file. is there anything i can do to optimize the CPU so it does not work so hard? as you can see, i only care about person detection as I already have an Amcrest NVR to record videos 24/7. i would like to add 2 more 4K camera at the full 4K resolution instead of the smaller feed of 640x480
detectors:
cpu1:
type: cpu
mqtt:
host: 192.168.1.229
port: 1883
# Optional: topic prefix (default: shown below)
# WARNING: must be unique if you are running multiple instances
topic_prefix: frigate
# Optional: client id (default: shown below)
# WARNING: must be unique if you are running multiple instances
client_id: frigate
# Optional: user
user: mqtt2
# Optional:
# NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}.
# eg. password: '{FRIGATE_MQTT_PASSWORD}'
password: PASSWORD
# Optional: interval in seconds for publishing stats (default: shown below)
stats_interval: 60
cameras:
backyard:
ffmpeg:
inputs:
- path: rtsp://admin:[email protected]:554/cam/realmonitor?channel=1&subtype=1
roles:
- detect
- rtmp
width: 640
height: 480
fps: 10
rtmp:
# Required: Enable the live stream (default: True)
enabled: true
snapshots:
# Optional: Enable writing jpg snapshot to /media/frigate/clips (default: shown below)
# This value can be set via MQTT and will be updated in startup based on retained value
enabled: true
# Optional: print a timestamp on the snapshots (default: shown below)
timestamp: true
# Optional: draw bounding box on the snapshots (default: shown below)
bounding_box: true
# Optional: crop the snapshot (default: shown below)
crop: False
# # Optional: height to resize the snapshot to (default: original size)
# height: 175
# Optional: Camera override for retention settings (default: global values)
retain:
# Required: Default retention days (default: shown below)
default: 10
# Optional: Per object retention days
objects:
person: 15
objects:
track:
- person