1. Deep Classifiers-Based License Plate Detection, Localization and Recognition on GPU-Powered Mobile Platform.
- Author
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Syed Tahir Hussain Rizvi, Patti, Denis, Björklund, Tomas, Cabodi, Gianpiero, and Francini, Gianluca
- Subjects
GRAPHICS processing units ,MOBILE communication systems ,AUTOMOBILE license plates - Abstract
The realization of a deep neural architecture on a mobile platform is challenging, but can open up a number of possibilities for visual analysis applications. A neural network can be realized on a mobile platform by exploiting the computational power of the embedded GPU and simplifying the flow of a neural architecture trained on the desktop workstation or a GPU server. This paper presents an embedded platform-based Italian license plate detection and recognition system using deep neural classifiers. In this work, trained parameters of a highly precise automatic license plate recognition (ALPR) system are imported and used to replicate the same neural classifiers on a Nvidia Shield K1 tablet. A CUDA-based framework is used to realize these neural networks. The flow of the trained architecture is simplified to perform the license plate recognition in real-time. Results show that the tasks of plate and character detection and localization can be performed in real-time on a mobile platform by simplifying the flow of the trained architecture. However, the accuracy of the simplified architecture would be decreased accordingly. [ABSTRACT FROM AUTHOR]
- Published
- 2017
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