My Frigate setup does a fantastic detecting for “car” and “person”. So, I tried implementing dog, cat, and yes raccoon detection for my backyard grass area. The idea was as soon as a family of raccoons were detected, digging the grass for warms, the sprinkler valves would turn, to deter the raccoon family!
Unfortunately, I am having a hard time getting my cameras and Frigate to detect any animal the size of a raccoon. I have tried many changes with success.
Here is my frigate.yaml file for one camera7, showing different filter methods. I would appreciate your help. Thank you
detectors:
coral:
type: edgetpu
device: usb
mqtt:
host: IP
user: USER_NAME
password: PASSWORD
#--------------------------------
# <------ Name the camera 7 BEGIN
#--------------------------------
camera7:
enabled: true
ffmpeg:
#hwaccel_args: # recommended By ChatGPT. Remove if you see no difference
# - -hwaccel # recommended By ChatGPT. Remove if you see no difference
# - qsv # recommended By ChatGPT. Remove if you see no difference
output_args:
record: preset-record-generic-audio-copy
inputs:
- path: rtsp://admin:PASSWORD@IP:554/cam/realmonitor?channel=7&subtype=1
input_args: preset-rtsp-restream
roles:
- detect
- path: rtsp://admin:PASSWORD@IP:554:554/cam/realmonitor?channel=7&subtype=0
roles:
- record
detect:
width: 1270 # <- optional, by default Frigate tries to automatically detect resolution
height: 720 # <- optional, by default Frigate tries to automatically detect resolution
fps: 5
stationary:
interval: 20
threshold: 50
# object track ????
objects:
track:
- person
- dog
- cat
- raccoon # Add raccoon to the list
filters: # suggested config CPT
person:
min_area: 6000
max_area: 150000
min_score: 0.6
threshold: 0.6
dog:
min_area: 4000
max_area: 120000
min_score: 0.5
threshold: 0.6
cat:
min_area: 2000
max_area: 80000
min_score: 0.5
threshold: 0.6
raccoon:
min_area: 3000
max_area: 100000
min_score: 0.5
threshold: 0.6
# filters: # original config
# person:
# min_area: 5000
# min_score: 0.5
# threshold: 0.6
# dog:
# min_area: 5000
# min_score: 0.5
# threshold: 0.6
# cat:
# min_area: 5000
# min_score: 0.5
# threshold: 0.6
# raccoon: # Add filter for raccoon
# min_area: 5000
# min_score: 0.5
# threshold: 0.6
# Optional: Record configuration
record:
enabled: true
retain:
days: 3
mode: active_objects #options: all motion active_objects
events:
pre_capture: 20
post_capture: 20
retain:
default: 3
mode: active_objects #options: All motion active_objects
# Optional: Objects to save recordings for. (default: all tracked objects)
objects:
- person
- dog
- cat
- raccoon
# Optional: Per object retention days
# objects: 4
# person: 7
# Optional: Configuration for the jpg snapshots written to the clips directory for each event
snapshots:
enabled: true
timestamp: true
bounding_box: true
crop: false
retain:
default: 3
zones:
back2_zone1:
coordinates: 0.005,0.993,0,0.219,0.228,0.14,0.202,0.212,0.661,0.189,0.994,0.551,0.994,0.994
loitering_time: 0
objects: person
inertia: 3
back2_zone2:
coordinates:
0.108,0.115,0.195,0.087,0.305,0.068,0.483,0.069,0.5,0.052,0.556,0.06,0.659,0.178,0.22,0.196,0.249,0.125,0.082,0.171
loitering_time: 0
objects:
- person
- dog
- cat
- raccoon
inertia: 3
motion:
mask: 0.752,0.035,0.75,0.077,0.906,0.082,0.904,0.034
review:
alerts:
required_zones:
- back2_zone1
- back2_zone2
detections:
required_zones:
- back2_zone1
- back2_zone2