729f5c0833
* use a small yolov9 model for detection * use yolov9 for users without frigate+ and update retention algorithm * new lpr config fields * levenshtein distance package * tweaks * docs
2.7 KiB
| id | title |
|---|---|
| license_plate_recognition | License Plate Recognition (LPR) |
Frigate can recognize license plates on vehicles and automatically add the detected characters 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 with a dedicated LPR camera.
Users running a Frigate+ model 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.
LPR is most effective when the vehicle’s license plate is fully visible to the camera. For moving vehicles, Frigate will attempt to read the plate continuously, refining its detection and keeping the most confident result. LPR will not run on stationary vehicles.
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
Advanced Configuration
Several options are available to fine-tune the LPR feature. For example, you can adjust the min_area setting, which defines the minimum size in pixels a license plate must be before LPR runs. The default is 1000 pixels.
The min_plate_length field specifies the minimum number of characters a license plate must have to be added to the object as a sub label.
If you want to allow a number of number of missing/incorrect characters to still cause a detected plate to match a known plate, set the match_distance field. For example, setting match_distance to 1 would cause a detected plate of ABCDE to match ABCBE or ABCD.
Additionally, you can define known_plates as strings or regular expressions, allowing Frigate to label tracked vehicles with custom sub_labels when a recognized plate is detected. This information is then accessible in the UI, filters, and notifications.
lpr:
enabled: true
min_area: 1500
min_plate_length: 4
match_distance: 1
known_plates:
Wife's Car:
- "ABC-1234"
- "ABC-I234"
Johnny:
- "J*N-*234" # Using wildcards for H/M and 1/I
Sally:
- "[S5]LL-1234" # Matches SLL-1234 and 5LL-1234
In this example, "Wife's Car" will appear as the label for any vehicle matching the plate "ABC-1234." The model might occasionally interpret the digit 1 as a capital I (e.g., "ABC-I234"), so both variations are listed. Similarly, multiple possible variations are specified for Johnny and Sally.