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There are different versions of the vehicle recognition system available on the market, but as a rule, their hardware and software are based on Al technologies. Generally, these systems have a wide range of capabilities, such as automatically detecting and determining the vehicle type and license plate in real-time and then storing and cross referencing it to white/black lists.
Al for LPR
The degree to which Al technologies are applied plays a great role in the work of vehicle detection systems and many tasks are solved more efficiently with Al than with standard mechanisms of the past.
For example, the TRASSIR system's neural network is able to accurately determine the coordinates of a license plate's corners (even if it is located at an angle to the camera), which allows it to record the car's data as accurately as possible. Experience has proven that the system's implementation allows users to save money on the maintenance and development of facilities in the fields of construction, retail and industry. In some cases, vehicle control time at the checkpoint was reduced by half.
The latest improvements in the field of auto detection have been aimed at improving vehicle tracking: since vehicles can differ visually from one another greatly, the neural network calculates a vector of unique features of the car to improve recognition accuracy.
Market trends are such that systems are constantly being improved, as manufacturers continuously collect feedback from users. We can look at the following areas to improve the module:
The six steps of license plate recognition.
As a rule of thumb, most license plate recognition algorithms follow the steps below:
1. Firstly, the LPR engine looks to identify the license plate's positioning within the image - this helps the LPR focus only on the license plate and disregard any other data.
2. Angular corrections help the LRP decode license plates that have been captured at awkward angles - for example, on the side or from above.
3. Filters are then applied to help eliminate any shadows or shaded areas. Edge detection in particular is used when there is high contrast between the background and the text being identified.
4. Tools like whitespace delineation are used to identify the spaces between letters on the license plate. Errors are more likely to occur here if the spacing of characters on the license plate varies.
5. Optical character recognition techniques are then used to identify each character. This could include pattern matching, proportion, pixel repetition, and edge tracing.
6. In the final step the characters identified and their sequence are checked against rules specific to each region.
Follow the link below to learn more:
https://www.securitymiddleeastmag.com/magazine/
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