Skip to main content

License Plate Recognition (LPR)

Frigate can recognize license plates on vehicles and automatically add the detected characters or recognized name as a sub_label to objects that are of type car. A common use case may be to read the license plates of cars pulling into a driveway or cars passing by on a street.

LPR works best when the license plate is clearly visible to the camera. For moving vehicles, Frigate continuously refines the recognition process, keeping the most confident result. However, LPR does not run on stationary vehicles.

When a plate is recognized, the detected characters or recognized name is:

  • Added as a sub_label to the car tracked object.
  • Viewable in the Review Item Details pane in Review and the Tracked Object Details pane in Explore.
  • Filterable through the More Filters menu in Explore.
  • Published via the frigate/events MQTT topic as a sub_label for the tracked object.

Model Requirements​

Users running a Frigate+ model (or any custom model that natively detects license plates) should ensure that license_plate is added to the list of objects to track either globally or for a specific camera. This will improve the accuracy and performance of the LPR model.

Users without a model that detects license plates can still run LPR. Frigate uses a lightweight YOLOv9 license plate detection model that runs on your CPU. In this case, you should not define license_plate in your list of objects to track.

note

Frigate needs to first detect a car before it can recognize a license plate. If you're using a dedicated LPR camera or have a zoomed-in view, make sure the camera captures enough of the car for Frigate to detect it reliably.

Minimum System Requirements​

License plate recognition works by running AI models locally on your system. The models are relatively lightweight and run on your CPU. At least 4GB of RAM is required.

Configuration​

License plate recognition is disabled by default. Enable it in your config file:

lpr:
  enabled: True

Ensure that your camera is configured to detect objects of type car, and that a car is actually being detected by Frigate. Otherwise, LPR will not run.

Like the other real-time processors in Frigate, license plate recognition runs on the camera stream defined by the detect role in your config. To ensure optimal performance, select a suitable resolution for this stream in your camera's firmware that fits your specific scene and requirements.

Advanced Configuration​

Fine-tune the LPR feature using these optional parameters:

Detection​

  • detection_threshold: License plate object detection confidence score required before recognition runs.
    • Default: 0.7
    • Note: If you are using a Frigate+ model and you set the threshold in your objects config for license_plate higher than this value, recognition will never run. It's best to ensure these values match, or this detection_threshold is lower than your object config threshold.
  • min_area: Defines the minimum size (in pixels) a license plate must be before recognition runs.
    • Default: 1000 pixels.
    • Depending on the resolution of your camera's detect stream, you can increase this value to ignore small or distant plates.

Recognition​

  • recognition_threshold: Recognition confidence score required to add the plate to the object as a sub label.
    • Default: 0.9.
  • min_plate_length: Specifies the minimum number of characters a detected license plate must have to be added as a sub label to an object.
    • Use this to filter out short, incomplete, or incorrect detections.
  • format: A regular expression defining the expected format of detected plates. Plates that do not match this format will be discarded.
    • "^[A-Z]{1,3} [A-Z]{1,2} [0-9]{1,4}$" matches plates like "B AB 1234" or "M X 7"
    • "^[A-Z]{2}[0-9]{2} [A-Z]{3}$" matches plates like "AB12 XYZ" or "XY68 ABC"
    • Websites like https://regex101.com/ can help test regular expressions for your plates.

Matching​

  • known_plates: List of strings or regular expressions that assign custom a sub_label to car objects when a recognized plate matches a known value.
    • These labels appear in the UI, filters, and notifications.
  • match_distance: Allows for minor variations (missing/incorrect characters) when matching a detected plate to a known plate.
    • For example, setting match_distance: 1 allows a plate ABCDE to match ABCBE or ABCD.
    • This parameter will not operate on known plates that are defined as regular expressions. You should define the full string of your plate in known_plates in order to use match_distance.

Configuration Examples​

lpr:
  enabled: True
  min_area: 1500 # Ignore plates smaller than 1500 pixels
  min_plate_length: 4 # Only recognize plates with 4 or more characters
  known_plates:
    Wife's Car:
      - "ABC-1234"
      - "ABC-I234" # Accounts for potential confusion between the number one (1) and capital letter I
    Johnny:
      - "J*N-*234" # Matches JHN-1234 and JMN-I234, but also note that "*" matches any number of characters
    Sally:
      - "[S5]LL 1234" # Matches both SLL 1234 and 5LL 1234
    Work Trucks:
      - "EMP-[0-9]{3}[A-Z]" # Matches plates like EMP-123A, EMP-456Z
lpr:
  enabled: True
  min_area: 4000 # Run recognition on larger plates only
  recognition_threshold: 0.85
  format: "^[A-Z]{2} [A-Z][0-9]{4}$" # Only recognize plates that are two letters, followed by a space, followed by a single letter and 4 numbers
  match_distance: 1 # Allow one character variation in plate matching
  known_plates:
    Delivery Van:
      - "RJ K5678"
      - "UP A1234"
    Supervisor:
      - "MN D3163"

FAQ​

Why isn't my license plate being detected and recognized?​

Ensure that:

  • Your camera has a clear, human-readable, well-lit view of the plate. If you can't read the plate, Frigate certainly won't be able to. This may require changing video size, quality, or frame rate settings on your camera, depending on your scene and how fast the vehicles are traveling.
  • The plate is large enough in the image (try adjusting min_area) or increasing the resolution of your camera's stream.
  • A car is detected first, as LPR only runs on recognized vehicles.

If you are using a Frigate+ model or a custom model that detects license plates, ensure that license_plate is added to your list of objects to track. If you are using the free model that ships with Frigate, you should not add license_plate to the list of objects to track.

Can I run LPR without detecting car objects?​

No, Frigate requires a car to be detected first before recognizing a license plate.

How can I improve detection accuracy?​

  • Use high-quality cameras with good resolution.
  • Adjust detection_threshold and recognition_threshold values.
  • Define a format regex to filter out invalid detections.

Does LPR work at night?​

Yes, but performance depends on camera quality, lighting, and infrared capabilities. Make sure your camera can capture clear images of plates at night.

How can I match known plates with minor variations?​

Use match_distance to allow small character mismatches. Alternatively, define multiple variations in known_plates.

How do I debug LPR issues?​

  • View MQTT messages for frigate/events to verify detected plates.
  • Adjust detection_threshold and recognition_threshold settings.
  • If you are using a Frigate+ model or a model that detects license plates, watch the debug view (Settings --> Debug) to ensure that license_plate is being detected with a car.
  • Enable debug logs for LPR by adding frigate.data_processing.common.license_plate: debug to your logger configuration. These logs are very verbose, so only enable this when necessary.

Will LPR slow down my system?​

LPR runs on the CPU, so performance impact depends on your hardware. Ensure you have at least 4GB RAM and a capable CPU for optimal results.