We report an access control system based on automatic license plate recognition, consisting of three main modules for acquisition, extraction, and recognition. The basic idea is to couple the online learning of a neural background model with a stopped foreground subtraction mechanism to efficiently provide a subset of relevant video frames where to look for. Another key point is the use of matching the entire license plate ROI with those stored in a database of authorized license plates, based on suitable features and validation tests. Experimental results confirm that the proposed system attains overall performance comparable with that of the state-of-the-art ALPR methods.


Advances in Neural Networks: Computational and Theoretical Issues

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Di Nardo E., Maddalena L., Petrosino A.

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@incollection{di2015video, title={Video-Based Access Control by Automatic License Plate Recognition}, author={Di Nardo, Emanuel and Maddalena, Lucia and Petrosino, Alfredo}, booktitle={Advances in Neural Networks: Computational and Theoretical Issues}, pages={103–117}, year={2015}, publisher={Springer} }

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