Deep Artwork Detection and Retrieval for Automatic Context-Aware Audio Guides
Lorenzo Seidenari, Claudio Baecchi, Tiberio Uricchio, Andrea Ferracani, Marco Bertini, Alberto Del Bimbo · ACM Transactions on Multimedia Computing Communications and Applications · 2017
In this article, 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 or she 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 of working 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 an NVIDIA Jetson TK1 and a NVIDIA Shield Tablet K1 and tested in a real-world environment (Bargello Museum of Florence).