Frigate can recognize license plates on vehicles and automatically add the detected characters to the `recognized_license_plate` field or a known name as a `sub_label` to tracked objects 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.
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](https://docs.frigate.video/plus/#available-label-types) 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.
In the default mode, Frigate's LPR needs to first detect a `car` before it can recognize a license plate. If you're using a dedicated LPR camera and have a zoomed-in view where a `car` will not be detected, you can still run LPR, but the configuration parameters will differ from the default mode. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section below.
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:
You can also enable it for specific cameras only at the camera level:
```yaml
cameras:
driveway:
...
lpr:
enabled: True
```
For non-dedicated LPR cameras, 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.
- Note: This is field only applies to the standalone license plate detection model, `min_score` should be used to filter for models that have license plate detection built in.
- **`recognition_threshold`**: Recognition confidence score required to add the plate to the object as a `recognized_license_plate` and/or `sub_label`.
- **`min_plate_length`**: Specifies the minimum number of characters a detected license plate must have to be added as a `recognized_license_plate` and/or `sub_label` to an object.
- **`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.
- 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`.
These configuration parameters are available at the global level of your config. The only optional parameters that should be set at the camera level are `enabled` and `min_area`.
Dedicated LPR cameras are single-purpose cameras with powerful optical zoom to capture license plates on distant vehicles, often with fine-tuned settings to capture plates at night.
Users with a dedicated LPR camera can run Frigate's LPR by specifying a camera type of `lpr` in the camera configuration. An example config for a dedicated LPR camera might look like this:
```yaml
# LPR global configuration
lpr:
enabled: True
min_area: 2000
min_plate_length: 4
# Dedicated LPR camera configuration
cameras:
dedicated_lpr_camera:
type: "lpr" # required to use dedicated LPR camera mode
lpr:
enabled: True
expire_time: 3 # optional, default
ffmpeg: ...
detect:
enabled: False # optional, disable Frigate's standard object detection pipeline
fps: 5
width: 1920
height: 1080
motion:
threshold: 30
contour_area: 80 # use an increased value here to tune out small motion changes
improve_contrast: false
mask: 0.704,0.007,0.709,0.052,0.989,0.055,0.993,0.001 # ensure your camera's timestamp is masked
record:
enabled: True # disable recording if you only want snapshots
detections:
enabled: True
retain:
default: 7
```
The camera-level `type` setting tells Frigate to treat your camera as a dedicated LPR camera. Setting this option bypasses Frigate's standard object detection pipeline so that a `car` does not need to be detected to run LPR. This dedicated LPR pipeline does not utilize defined zones or object masks, and the license plate detector is always run on the full frame whenever motion activity occurs. If a plate is found, a snapshot at the highest scoring moment is saved as a `car` object, visible in Explore and searchable by the recognized plate via Explore's More Filters.
An optional config variable for dedicated LPR cameras only, `expire_time`, can be specified under the `lpr` configuration at the camera level to change the time it takes for Frigate to consider a previously tracked plate as expired.
:::note
When using `type: "lpr"` for a camera, a non-standard object detection pipeline is used. Any detected license plates on dedicated LPR cameras are treated similarly to manual events in Frigate. Note that for `car` objects with license plates:
- Review items will always be classified as a `detection`.
- Snapshots will always be saved.
- Tracked objects are retained according to your retain settings for `record` and `snapshots`.
- Zones and object masks cannot be used.
- The `frigate/events` MQTT topic will not publish tracked object updates, though `frigate/reviews` will if recordings are enabled.
:::
### Best practices for using Dedicated LPR camera mode
- Tune your motion detection and increase the `contour_area` until you see only larger motion boxes being created as cars pass through the frame (likely somewhere between 50-90 for a 1920x1080 detect stream). Increasing the `contour_area` filters out small areas of motion and will prevent excessive resource use from looking for license plates in frames that don't even have a car passing through it.
- Disable the `improve_contrast` motion setting, especially if you are running LPR at night and the frame is mostly dark. This will prevent small pixel changes and smaller areas of motion from triggering license plate detection.
- Ensure your camera's timestamp is covered with a motion mask so that it's not incorrectly detected as a license plate.
- While not strictly required, it may be beneficial to disable standard object detection on your dedicated LPR camera (`detect` --> `enabled: False`). If you've set the camera type to `"lpr"`, license plate detection will still be performed on the entire frame when motion occurs.
- If multiple tracked objects are being produced for the same license plate, you can tweak the `expire_time` to prevent plates from being expired from the view as quickly.
- You may need to change your camera settings for a clearer image or decrease your global `recognition_threshold` config if your plates are not being accurately recognized at night.
- 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.
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?
In normal LPR mode, Frigate requires a `car` to be detected first before recognizing a license plate. If you have a dedicated LPR camera, you can change the camera `type` to `"lpr"` to use the Dedicated LPR Camera algorithm. This comes with important caveats, though. See the [Dedicated LPR Cameras](#dedicated-lpr-cameras) section above.
- 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.
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. If you are running the Dedicated LPR Camera mode, resource usage will be higher compared to users who run a model that natively detects license plates. Tune your motion detection settings for your dedicated LPR camera so that the license plate detection model runs only when necessary.
