License Plate Recognition: Main Market Use Cases
The main market use cases for license plate recognition are constrained and in the wild.
Constrained Scenarios
In constrained license plate recognition scenarios:
A specialized LPR camera is usually used.
The camera has a single purpose and is positioned in a dedicated way.
The camera is in a controlled (constrained) environment where we know the conditions (example: lighting) and how the vehicles will behave (example: speed) and sometimes constrain the behavior (example: speed bumps), such as gates in an entrance or exit of a parking lot.
In these scenarios, the accuracy rate is usually high since the camera is positioned in an ideal way, usually on the vehicle’s level, its field of view (FOV) is calibrated towards the expected location of the plate and the lighting is optimized. In addition, vehicles often stop before the gate so there is no motion blur. Another example is a toll road billing system where vehicles can be captured in full speed – but the profile is known in advance, so the camera and lighting systems are optimally designed accordingly.
In the Wild Scenarios
In the wild license plate recognition occurs in a non-controlled environment.
In in the wild scenarios:
The position of the camera is not specifically designed or designated for license plate recognition but as general surveillance cameras.
The lighting is not always controlled.
The FOV can be wide so there are much less pixels (resolution) per each plate.
Even the quality of recorded video is usually optimized for storage efficiency rather than for quality.
BriefCam’s Main LPR Use Cases
BriefCam’s license plate recognition’s main use cases are:
Selecting a vehicle with a recognized plate or manually entering a plate number or watchlist and finding its appearances in this and other videos or cameras (such as tracking its route). This is done in BriefCam’s REVIEW module.
Setting a rule to alert when a license plate that is on a predefined watchlist (or watchlists) appears. This is done in BriefCam’s RESPOND module.
Setting a rule to alert when a license plate that appears in a video is not on the watchlist, for example if a non-employee car appears on the company parking lot. This is done in BriefCam’s RESPOND module.
Measuring vehicle speeds based on the average time it takes to travel between cameras with a known distance, quantifying trends using vehicle appearance counts in a specific scene or determining trends in vehicles’ routes according to the sequence of appearances across different cameras. This is done in BriefCam’s RESEARCH module.