\chapter{Deep Artwork Detection and Retrieval for Context Aware Smart Audio Guides}\label{chap:deep-artwork}
{

\begin{quote}
\textit{
In this Chapter we address the problem of creating a smart audio guide that adapts to the actions and interests of museum visitors. As an autonomous agent, our guide perceives the context and is able to interact with users in an appropriate fashion. To do so, it understands what the visitor is looking at, if the visitor is moving inside the museum hall or if he is talking with a friend. The guide performs automatic recognition of artworks, and it provides configurable interface features to improve the user experience and the fruition of multimedia materials through semi-automatic interaction. Our smart audio guide is backed by a computer vision system capable to work in real-time on a mobile device, coupled with audio and motion sensors. We propose the use of a compact Convolutional Neural Network (CNN) that performs object classification and localization. Using the same CNN features computed for these tasks, we perform also robust artwork recognition. To improve the recognition accuracy we perform additional video processing using shape based filtering, artwork tracking and temporal filtering. The system has been deployed on a NVIDIA Jetson TK1 and a NVIDIA Shield Tablet K1, and tested in a real world environment (Bargello Museum of Florence). \footnote{This chapter has been published as \dquote{Deep Artwork Detection and Retrieval for Automatic Context-Aware Audio Guides} in \textit{ACM Transactions on Multimedia Computing Communications and Applications, 35, pp.~1-21, 2017}~\cite{seidenari2017} and as \dquote{Portable computer vision for new intelligent audio guides} in \textit{EVA 2017 Florence - Electroning Imaging \& the Visual Arts, 2017}~\cite{eva2017}}}
\end{quote}
