| Title |
Building TensorFlow Applications in Smart City Scenarios |
| ID_Doc |
39667 |
| Authors |
Mulfari, D; Minnolo, AL; Puliafito, A |
| Title |
Building TensorFlow Applications in Smart City Scenarios |
| Year |
2017 |
| Published |
|
| DOI |
|
| Abstract |
Nowadays, visual object recognition is one of the key applications for computer vision and deep learning techniques. With the recent development in mobile computing technology, many deep learning framework software support Personal Digital Assistant systems, i.e., smart phones or tablets, allowing developers to conceive innovative applications. In this work, we intend to employ such ICT strategies with the aim of supporting the tourism in an art city: for these reasons, we propose to provide tourists with a mobile application in order to better explore artistic heritage within an urban environment by using just their smartphone's camera. The software solution is based on Google TensorFlow, an innovative deep learning framework mainly designed for pattern recognition tasks. The paper presents our design choices and an early performance evaluation. |
| Author Keywords |
Smart cities; Artistic heritage; Deep learning; Google TensorFlow |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000411757300064 |
| WoS Category |
Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Information Systems; Computer Science, Software Engineering |
| Research Area |
Computer Science |
| PDF |
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