The diffusion of powerful mobile devices has posed the basis for new applications implementing on the devices (which are embedded devices) sophisticated computer vision and pattern recognition algorithms. This paper describes the implementation of a complete system for automatic recognition of places localized on a map through the recognition of significant signs by means of the camera of a mobile device (smartphone, tablet, etc.). The paper proposes a novel classification algorithm based on the innovative use of bag-of-words on ORB features. The recognition is achieved using a simple yet effective search scheme which exploits GPS localization to limit the possible matches. This simple solution brings several advantages, such as the speed also on limited-resource devices, the usability also with limited training samples and the easiness of adapting to new training samples and classes. The overall architecture of the system is based on a REST-JSON client-server architecture. The experimental results have been conducted in a real scenario and evaluating the different parameters which influence the performance.

Lightweight sign recognition for mobile devices / Michele, Fornaciari; Prati, Andrea; Costantino, Grana; Rita, Cucchiara. - (2013), pp. 1-6. [10.1109/ICDSC.2013.6778220]

Lightweight sign recognition for mobile devices

PRATI, Andrea;
2013

Abstract

The diffusion of powerful mobile devices has posed the basis for new applications implementing on the devices (which are embedded devices) sophisticated computer vision and pattern recognition algorithms. This paper describes the implementation of a complete system for automatic recognition of places localized on a map through the recognition of significant signs by means of the camera of a mobile device (smartphone, tablet, etc.). The paper proposes a novel classification algorithm based on the innovative use of bag-of-words on ORB features. The recognition is achieved using a simple yet effective search scheme which exploits GPS localization to limit the possible matches. This simple solution brings several advantages, such as the speed also on limited-resource devices, the usability also with limited training samples and the easiness of adapting to new training samples and classes. The overall architecture of the system is based on a REST-JSON client-server architecture. The experimental results have been conducted in a real scenario and evaluating the different parameters which influence the performance.
9781479921645
Lightweight sign recognition for mobile devices / Michele, Fornaciari; Prati, Andrea; Costantino, Grana; Rita, Cucchiara. - (2013), pp. 1-6. [10.1109/ICDSC.2013.6778220]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11381/2809237
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