[Bug]: Snapshot for Person in Zone Sporadically Not Sending via MQTT

[rogerquake]

Checklist

• I have updated to the latest available Frigate version.
• I have cleared the cache of my browser.
• I have tried a different browser to see if it is related to my browser.
• I have tried reproducing the issue in incognito mode to rule out problems with any third party extensions or plugins I have installed.
• I have asked the AI at https://docs.frigate.video about my issue.

Describe the problem you are having

I'm having sporadic issue where a person is successfully detected by Frigate in a zone created for my front door, however the person snapshot is not being sent via MQTT so it appears in HomeAssistant. In the example below, the person was detectedin the front-door-zone and reached the 90% threshold, but the image for the person was never sent via MQTT.

I thought I had fixed this issue by including the zone in both the snapshot and MQTT required_zones settings however it's been sporadic if the person image is sent via MQTT or not. This only started to manifest itself after upgrading to the latest 16 release (was on an older 15 release).

I'm going to try forgoing required_zones and using an object mask to achieve the same results (which I know is not recommended), but I need this to work reliability like it has been for the many years I've been using Frigate.

Steps to reproduce

1. Person enters zone
2. See that they register inside the zone inside Frigate UI
3. Person image for the associated camera not updated inside HomeAssistant. Next detection for a person on that camera it will eventually get updated.
   ...

Version

0.16.2

In which browser(s) are you experiencing the issue with?

No response

Frigate config file

mqtt:
  host: HA
  port: 1883
  topic_prefix: frigate
  client_id: frigate
  user: username
  password: password

logger:
  logs:
    frigate.record: debug
    frigate.go2rtc: debug

detectors:
  # Required: name of the detector
  onnx:
    type: onnx


go2rtc:
  streams:

    front-door:
      - rtsp://viewer:Live33308@CAMERAIP:554/Streaming/Channels/101
    front-door-mobile:
      - rtsp://viewer:Live33308@CAMERAIP:554/Streaming/Channels/102
    front-door-detect:
      - rtsp://viewer:Live33308@CAMERAIP:554/Streaming/Channels/103


  webrtc:
    candidates:
      - MyIP:8555
      - stun:8555
    mode: webrtc/tcp

model:
  path: plus:// YOLONAS Frigate+ Model Key


ffmpeg:
  hwaccel_args: preset-nvidia-h264

detect:
  # Optional: width of the frame for the input with the detect role (default: shown below)
  width: 1920
  # Optional: height of the frame for the input with the detect role (default: shown below)
  height: 1080
  # Optional: desired fps for your camera for the input with the detect role (default: shown below)
  # NOTE: Recommended value of 5. Ideally, try and reduce your FPS on the camera.
  fps: 6
  # Optional: enables detection for the camera (default: True)
  # This value can be set via MQTT and will be updated in startup based on retained value
  enabled: true
  # Optional: Number of frames without a detection before frigate considers an object to be gone. (default: 5x the frame rate)
  max_disappeared: 25

  # Optional: Configuration for stationary object tracking
  stationary:
    # Optional: Frequency for running detection on stationary objects (default: shown below)
    # When set to 0, object detection will never be run on stationary objects. If set to 10,
    # it will be run on every 10th frame.
    interval: 10
    # Optional: Number of frames without a position change for an object to be considered
    # stationary (default: 10x the frame rate or 10s)
    threshold: 50
    # Optional: Define a maximum number of frames for tracking a stationary object (default: not set, track forever)
    # This can help with false positives for objects that should only be stationary for a limited amount of time.
    # It can also be used to disable stationary object tracking. For example, you may want to set a value for person,
    # but leave car at the default.
    max_frames:
      # Optional: Default for all object types (default: not set, track forever)
      # Optional: Object specific values
      objects:
        person: 1000

  # Optional: Milliseconds to offset detect annotations by (default: shown below).
  annotation_offset: 0

# 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
    - dog
  # 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: 1000
      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
      #max_area: 400000
      # Optional: minimum score for the object to initiate tracking (default: shown below)
      min_score: 0.80
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      #threshold: 0.95
    dog:
      # Optional: minimum width*height of the bounding box for the detected object (default: 0)
      #min_area: 1000
      # Optional: maximum width*height of the bounding box for the detected object (default: 24000000)
      #max_area: 400000
      # Optional: minimum score for the object to initiate tracking (default: shown below)
      min_score: 0.82
      # Optional: minimum decimal percentage for tracked object's computed score to be considered a true positive (default: shown below)
      #threshold: 0.90
motion:
  # Optional: The threshold passed to cv2.threshold to determine if a pixel is different enough to be counted as motion. (default: shown below)
  # Increasing this value will make motion detection less sensitive and decreasing it will make motion detection more sensitive.
  # The value should be between 1 and 255.
  threshold: 15
  # Optional: Minimum size in pixels in the resized motion image that counts as motion (default: 30)
  # Increasing this value will prevent smaller areas of motion from being detected. Decreasing will
  # make motion detection more sensitive to smaller moving objects.
  # As a rule of thumb:
  #  - 15 - high sensitivity
  #  - 30 - medium sensitivity
  #  - 50 - low sensitivity
  contour_area: 30
  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging the motion delta across multiple frames (default: shown below)
  # Higher values mean the current frame impacts the delta a lot, and a single raindrop may register as motion.
  # Too low and a fast moving person wont be detected as motion.
  delta_alpha: 0.2
  # Optional: Alpha value passed to cv2.accumulateWeighted when averaging frames to determine the background (default: shown below)
  # Higher values mean the current frame impacts the average a lot, and a new object will be averaged into the background faster.
  # Low values will cause things like moving shadows to be detected as motion for longer.
  # https://www.geeksforgeeks.org/background-subtraction-in-an-image-using-concept-of-running-average/
  frame_alpha: 0.2
  # Optional: Height of the resized motion frame  (default: 50)
  # This operates as an efficient blur alternative. Higher values will result in more granular motion detection at the expense
  # of higher CPU usage. Lower values result in less CPU, but small changes may not register as motion.
  frame_height: 100

record:
  # Optional: Enable recording (default: shown below)
  enabled: true
  sync_recordings: true
  # Optional: Retention settings for recording
  retain:
    # Optional: Number of days to retain recordings regardless of events (default: shown below)
    # NOTE: This should be set to 0 and retention should be defined in events section below
    #       if you only want to retain recordings of events.
    days: 3
    # Optional: Mode for retention. Available options are: all, motion, and active_objects
    #   all - save all recording segments regardless of activity
    #   motion - save all recordings segments with any detected motion
    #   active_objects - save all recording segments with active/moving objects
    # NOTE: this mode only applies when the days setting above is greater than 0
    mode: all
  # Optional: Event recording settings
  alerts:
    retain:
      days: 18
    pre_capture: 20
    post_capture: 20
  detections:
    retain:
      days: 18
    pre_capture: 20
    post_capture: 20
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: false
  # Optional: draw bounding box on the snapshots (default: shown below)
  bounding_box: false
  # Optional: crop the snapshot (default: shown below)
  crop: true
  # Optional: height to resize the snapshot to (default: original size)
  #height: 175
  # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
  required_zones: []
  # Optional: Camera override for retention settings (default: global values)
  retain:
    # Required: Default retention days (default: shown below)
    default: 18
    # Optional: Per object retention days
    objects:
      person: 18

live:
  # Optional: Set the height of the live stream. (default: 720)
  # This must be less than or equal to the height of the detect stream. Lower resolutions
  # reduce bandwidth required for viewing the live stream. Width is computed to match known aspect ratio.
  height: 720
  # Optional: Set the encode quality of the live stream (default: shown below)
  # 1 is the highest quality, and 31 is the lowest. Lower quality feeds utilize less CPU resources.
  quality: 8

  front-door:
    ffmpeg:
      input_args: preset-rtsp-restream
      output_args:
        record: preset-record-generic-audio-copy
      inputs:
        - path: rtsp://127.0.0.1:8554/front-door-detect
          roles:
            - detect

        - path: rtsp://127.0.0.1:8554/front-door
          roles:
            - record

    detect:
      width: 1920
      height: 1080
      fps: 6

    objects:
      track:
        - person
        - package

    zones:
      front-door-zone:
        coordinates: 
          0.633,0,0.591,0,0.574,0.324,0.275,0.335,0,0.373,0,1,1,1,0.997,0
        loitering_time: 0
        filters:
          person:
            min_area: 30000
            max_area: 200000
            min_score: 0.80
            threshold: 0.90
        inertia: 2
        objects: person
    motion:
      mask: 0.166,0.149,0.161,0,0,0,0,0.204,0.084,0.178,0.096,0.175,0.109,0.169


    snapshots:
      # Optional: Restrict snapshots to objects that entered any of the listed zones (default: no required zones)
      required_zones:
        - front-door-zone


    mqtt:
      # Optional: Enable publishing snapshot via mqtt for camera (default: shown below)
      # NOTE: Only applies to publishing image data to MQTT via 'frigate/<camera_name>/<object_name>/snapshot'.
      # All other messages will still be published.
      enabled: true
      # Optional: print a timestamp on the snapshots (default: shown below)
      timestamp: false
      # Optional: draw bounding box on the snapshots (default: shown below)
      bounding_box: false
      # Optional: crop the snapshot (default: shown below)
      crop: true
      # Optional: height to resize the snapshot to (default: shown below)
      height: 1080
      # Optional: jpeg encode quality (default: shown below)
      quality: 100
      required_zones: [front-door-zone]
    review:
      alerts: {}
      detections: {}

docker-compose file or Docker CLI command

services:
  frigate:
    container_name: frigate
    privileged: true # this may not be necessary for all setups
    restart: unless-stopped
    image: ghcr.io/blakeblackshear/frigate:0.16.2-tensorrt
    shm_size: "9G" # update for your cameras based on calculation above
    deploy:
      resources:
        reservations:
          devices:
            - driver: nvidia
              count: all # or specify a specific GPU ID
              capabilities: [gpu]
    environment:
      - NVIDIA_VISIBLE_DEVICES=all
      - NVIDIA_DRIVER_CAPABILITIES=compute,utility,video
      - FRIGATE_RTSP_PASSWORD=password
      - PLUS_API_KEY=KEY
    devices:
      - /dev/bus/usb:/dev/bus/usb # passes the USB Coral, needs to be modified for other versions
    volumes:
      - /etc/localtime:/etc/localtime:ro
      - /home/roger/docker-compose/frigate:/config
      - /frigate_recordings:/media/frigate
      - type: tmpfs # Optional: 1GB of memory, reduces SSD/SD Card wear
        target: /tmp/cache
        tmpfs:
          size: 28000000000
    ports:
      - "5000:5000"
      - "1935:1935" # RTMP feeds
      - "8554:8554" # RTSP feeds
      - "8555:8555/tcp" # WebRTC over tcp
      - "8555:8555/udp" # WebRTC over udp
      - "1984:1984/tcp" # WebRTC over udp

Relevant Frigate log output

NA

Relevant go2rtc log output

NA

Operating system

Home Assistant OS

Install method

Home Assistant Add-on

Network connection

Wired

Camera make and model

DS-2CD2187G2-LSU

Screenshots of the Frigate UI's System metrics pages

Any other information that may be helpful

No response

[inkeep-ai-assistant[bot]]

(1), (2), (3), (4), (5), (6), (7), (8), (9), (10)

Based on your configuration, the issue appears to be related to how zone requirements interact with MQTT snapshot publishing. Here are several configuration aspects to verify:

Zone Configuration Issues

Your zone is properly defined with coordinates and filters(1), but there are some considerations:

1. Zone presence evaluation: Zones evaluate presence based on the bottom center of the bounding box(1). If the person's bottom center point doesn't consistently fall within your front-door-zone polygon during detection, the snapshot won't be published via MQTT.

2. Inertia setting: You have inertia: 2 configured, which means the object must be present in the zone for 2 consecutive frames before being considered in the zone(1). This could cause timing issues where the person is detected but not yet "officially" in the zone when the snapshot would be taken.

MQTT Snapshot Configuration

Your MQTT configuration has required_zones: [front-door-zone](3)(5), which restricts MQTT messages to objects that entered the listed zones. The key issue is that both your snapshots section and mqtt section have required_zones configured(2)(5).

Recommended Fixes

1. Remove or adjust required_zones from the MQTT section: Since you already have required_zones in the snapshots section, having it in both places may create conflicts. Try removing it from the mqtt section:

mqtt:
  enabled: true
  timestamp: false
  bounding_box: false
  crop: true
  height: 1080
  quality: 100
  # Remove or comment out: required_zones: [front-door-zone]

(3)(5)

2. Adjust zone inertia: Consider reducing inertia to 1 or removing it entirely to make zone entry detection more immediate:

zones:
  front-door-zone:
    coordinates: 0.633,0,0.591,0,0.574,0.324,0.275,0.335,0,0.373,0,1,1,1,0.997,0
    loitering_time: 0
    inertia: 1  # Reduced from 2

(1)(2)

3. Verify zone polygon: Use the Debug view with Zones enabled to confirm your zone polygon properly covers the area where people walk(1)(4). The zone line should increase in thickness when objects enter.

Alternative Approach

If the issue persists, you could remove required_zones from both sections entirely and rely on object filters at the zone level to control what gets tracked(1)(7). This ensures snapshots are always published via MQTT when a person is detected, regardless of zone entry timing.

The sporadic nature of your issue suggests a race condition between zone entry detection and snapshot/MQTT publishing, which the above adjustments should resolve.

📚 Sources:

1. Zones
2. Full Reference Config
3. MQTT
4. Masks
5. Snapshots
6. Generative AI
7. Review
8. I have set up a custom model, but Frigate cannot recognize people
9. Feature Request - Add Zone(s) to tracked_object_update MQTT object
10. Utilizing MQTT to send one (and only one) snapshot per ID

---

Have another question? Just tag @inkeep.

[hawkeye217]

Closing as a duplicate of your original post here: #21027