Distinguish moving object vs stationary object

Hi ya. Giving frigate a try and am having problems getting it up and running. I’m installing amd64 version in Docker on my Linux server. Here’s the docker run command I’m using:

root@omv:/sharedfolders/appdata/frigate# docker run --rm --name frigate --privileged -v /dev/bus/usb:/dev/bus/usb -v /sharedfolders/appdata/frigate/:/config:ro -v /etc/localtime:/etc/localtime:ro -p 5000:5000 -e FRIGATE_RTSP_PASSWORD='redacted' blakeblackshear/frigate:stable-amd64

and here’s my config.yml file:

# Optional: port for http server (default: shown below)
web_port: 5000

# Optional: detectors configuration
# USB Coral devices will be auto detected with CPU fallback
detectors:
  # Required: name of the detector
  coral:
    # Required: type of the detector
    # Valid values are 'edgetpu' (requires device property below) and 'cpu'.
    type: edgetpu
    # Optional: device name as defined here: https://coral.ai/docs/edgetpu/multiple-edgetpu/#using-the-tensorflow-lite-python-api
    device: usb
    
# Required: mqtt configuration
mqtt:
  # Required: host name
  host: 192.168.0.25
  topic_prefix: frigate


# Optional: Global configuration for saving clips
save_clips:
  # Optional: Maximum length of time to retain video during long events. (default: shown below)
  # NOTE: If an object is being tracked for longer than this amount of time, the cache
  #       will begin to expire and the resulting clip will be the last x seconds of the event.
  max_seconds: 300
  # Optional: Location to save event clips. (default: shown below)
  clips_dir: /config/clips
  # Optional: Location to save cache files for creating clips. (default: shown below)
  # NOTE: To reduce wear on SSDs and SD cards, use a tmpfs volume.
  cache_dir: /config/cache

# Optional: Global object filters for all cameras.
# NOTE: can be overridden at the camera level
objects:
  # Optional: list of objects to track from labelmap.txt (default: shown below)
  track:
    - person
  # Optional: filters to reduce false positives for specific object types
  filters:
    person:
      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
      min_area: 5000
      # Optional: maximum width*height of the bounding box for the detected object (default: max_int)
      max_area: 100000
      # Optional: minimum score for the object to initiate tracking (default: shown below)
      min_score: 0.5
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      threshold: 0.85

# Required: configuration section for cameras
cameras:
  # Required: name of the camera
  front:
    # Required: ffmpeg settings for the camera
    ffmpeg:
      # Required: Source passed to ffmpeg after the -i parameter.
      # NOTE: Environment variables that begin with 'FRIGATE_' may be referenced in {}
      input: rtsp://admin:{FRIGATE_RTSP_PASSWORD}@192.168.0.26/livestream/11?action=play&media=video_audio_data
      # Optional: camera specific global args (default: inherit)
      global_args:
      # Optional: camera specific hwaccel args (default: inherit)
      hwaccel_args:
      # Optional: camera specific input args (default: inherit)
      input_args:
      # Optional: camera specific output args (default: inherit)
      output_args:
    
    # Optional: desired fps for your camera
    # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
    #       Frigate will attempt to autodetect if not specified.
    fps: 5


    # Optional: timeout for highest scoring image before allowing it
    # to be replaced by a newer image. (default: shown below)
    best_image_timeout: 60

    # Optional: camera specific mqtt settings
    mqtt:
      # Optional: crop the camera frame to the detection region of the object (default: False)
      crop_to_region: True
      # Optional: resize the image before publishing over mqtt
      snapshot_height: 300

    # Optional: Configuration for the snapshots in the debug view and mqtt
    snapshots:
      # Optional: print a timestamp on the snapshots (default: shown below)
      show_timestamp: True
      # Optional: draw zones on the debug mjpeg feed (default: shown below)
      draw_zones: False
      # Optional: draw bounding boxes on the mqtt snapshots (default: shown below)
      draw_bounding_boxes: True

    # Optional: Camera level object filters config. If defined, this is used instead of the global config.
    objects:
      track:
        - person
        - car
      filters:
        person:
          min_area: 5000
          max_area: 100000
          min_score: 0.5
          threshold: 0.85

and finally, here’s the error:

Fontconfig error: Cannot load default config file
On connect called
Traceback (most recent call last):
  File "detect_objects.py", line 441, in <module>
    main()
  File "detect_objects.py", line 202, in main
    ffmpeg_output_args = ["-r", str(config.get('fps'))] + ffmpeg_output_args
TypeError: can only concatenate list (not "NoneType") to list

I did try remove portions of the optional items in the yaml config file in hopes of getting things to work but I was not able. Can anyone tell me what the problem might be? I see that the error is for ‘fps’ and I do have it configured for the one camera that I’m trying. Is it not seeing this value?

Thanks for you help!

[blakeblackshear]

Should be easy enough. I have all the information I need to distinguish a moving object from a stationary object.

[noelhibbard]

Just wanted to give a +1 on this. I have a zone for my driveway and then wrote an app that monitors the MQTT events and then sends the thumbnail to a Telegram group. I only send the Telegrams if it’s in specific zones so I don’t get constant alerts on my phone. I typically park my cars all the way up the driveway in a masked area where I exclude car detection. This works well but on occasion I park outside of the mask and on nights like that I get constant notification every time there is motion. I’d love to see someway of filtering static objects. I’m sure this is easier said than done though.