[Config Support]: Stationnary cars - fine tuning false detection

[ChienDeLaCasse]

Describe the problem you are having

Greetings to all, forgive me in advance for my lack of IT knowledge.

I experience frequent false detections on stationary cars, highly taxing on storage.
I would be much grateful if you could share your views for root-cause (for instance : sun reflection? Leaves movement?) and best way to mitigate the issue. I have done my best to read Frigate documentation and similar discussions, but I think I am stuck in the "theory to practice" threshold.

Could I take your time for a kind of pratical "case analysis", and see how you would think ?

I wonder which combination of parameters (zones, min/max areas & ratios, object filters) would be most optimal.
One idea for instance, could be to set a min_area only to a specific bottom zone (where the cars are parked). But would that be the best approach ?

Do not hesitate to ask for any information I missed. I hope to benefit from your guidance and patience due to my beginner level. Thanks !

Version

0.13.2-6476F8A

Frigate config file

mqtt:
  enabled: false

ffmpeg:
  hwaccel_args: preset-vaapi

detectors:
  coral:
    type: edgetpu
    device: usb

record:
  enabled: True
  retain:
    days: 7
    mode: motion
  events:
    retain:
      default: 30
      mode: motion

snapshots:
  enabled: True
  retain:
    default: 30

objects:
  track:
    - person
    - cat
    - dog
    - car
    - bike
#  filters:
#    person:
#      min_area: 5000
#      max_area: 100000

cameras:
  FrontCam:
    ffmpeg:
      inputs:
        # High Resolution Stream
        - path: rtsp://admina:rootbeer123@192.168.1.79:554/H264/ch1/main/av_stream
          roles:
            - record
        # Low Resolution Stream
        - path: rtsp://admina:rootbeer123@192.168.1.79:554/H264/ch1/main/av_stream
          roles:
            - detect
    detect:
      width: 3632
      height: 1632
      fps: 8
    motion:
      mask:
        - 0,749,3632,689,3632,686,3632,0,0,0
        - 656,858,1288,892,1420,828,1325,753,989,660,652,739

Relevant log output

2024-05-07 14:54:06.378219838  [INFO] Preparing Frigate...
2024-05-07 14:54:06.395726299  [INFO] Starting Frigate...
2024-05-07 14:54:07.490546038  [2024-05-07 14:54:07] frigate.app                    INFO    : Starting Frigate (0.13.2-6476f8a)
2024-05-07 14:54:07.515536903  [2024-05-07 14:54:07] peewee_migrate.logs            INFO    : Starting migrations
2024-05-07 14:54:07.519263950  [2024-05-07 14:54:07] peewee_migrate.logs            INFO    : There is nothing to migrate
2024-05-07 14:54:07.526032621  [2024-05-07 14:54:07] frigate.app                    INFO    : Recording process started: 289
2024-05-07 14:54:07.526407893  [2024-05-07 14:54:07] frigate.app                    INFO    : go2rtc process pid: 97
2024-05-07 14:54:07.551623591  [2024-05-07 14:54:07] detector.coral                 INFO    : Starting detection process: 298
2024-05-07 14:54:10.164453634  [2024-05-07 14:54:07] frigate.app                    INFO    : Output process started: 300
2024-05-07 14:54:10.164561941  [2024-05-07 14:54:07] frigate.app                    INFO    : Camera processor started for FrontCam: 314
2024-05-07 14:54:10.164650285  [2024-05-07 14:54:07] frigate.app                    INFO    : Capture process started for FrontCam: 315
2024-05-07 14:54:10.165705522  [2024-05-07 14:54:07] frigate.detectors.plugins.edgetpu_tfl INFO    : Attempting to load TPU as usb
2024-05-07 14:54:10.168735622  [2024-05-07 14:54:10] frigate.detectors.plugins.edgetpu_tfl INFO    : TPU found
2024-05-07 14:59:07.600789056  [2024-05-07 14:59:07] frigate.storage                INFO    : Less than 1 hour of recording space left, running storage maintenance...
2024-05-07 14:59:07.627177130  [2024-05-07 14:59:07] frigate.storage                INFO    : Cleaned up 2787.599999999999 MB of recordings

Frigate stats

No response

Operating system

Proxmox

Install method

Docker Compose

Coral version

USB

Any other information that may be helpful

Please find snapshots which illustrate my situation.

Grey car "false detect" :

Black car "false detect":

[NickM-27]

you could try to use a max area or min_area filter but that might block out real detections too. This is one of those cases where the default model just struggles because it is not trained on images from cameras. We can see that in these cases even the "accurate" boxes are scoring really low

This is where something like Frigate+ or another improved model would likely work better. Frigate+ does allow you to specifically fine tune the model with your images so you can teach it that those boxes are not cars / correct the smaller boxes that are not capturing the full car.

[ChienDeLaCasse]

Hello, sorry my message got stuck on draft. Thank you for your input, I will keep experimenting (there is so much to learn about Frigate) and will consider Frigate+ if I cannot find a workaround.