In this demo we present a person re-identification system, based on cameras installed in an environment and featuring AI, that can be used in the design and development of human-centered multimedia applications intended to provide improved situational awareness and context-sensitive user-interfaces. Possible applications are related but not limited to user profiling and personalisation systems, multimedia recommendation, social context understanding and gamification, security, with objectives spanning from environment monitoring, cultural heritage fruition enhancement, retail trade promotion and assistive technologies provision in industry. In the context of the demonstration we are going to set up a system with several workstations equipped with cameras and contextual to paintings reproductions that simulate a museum exhibition in which users are tracked and re-identified at different locations. The data collected is used to enrich the user experience through a reactive voice interface that considers the user's visit, the artworks that most attracted the visitor's attention and the social context.

In this demo we present two applications designed for the cultural heritage domain that exploit gamification techniques in order to improve fruition and learning of museum artworks. The two applications encourage users to replicate the poses and facial expressions of characters from paintings or statues, to help museum visitors make connections with works of art. Both applications challenge the user to fulfill a task in a funny way and provide the user with a visual report of the his/her experience that can be shared on social media, improving the engagement of the museums, and providing information on the artworks replicated in the challenge.

NeuronUnityIntgration2.0 (demo video is avilable at http://tiny.cc/u1lz6y) is a plugin for Unity which provides gesture recognition functionalities through the Perception Neuron motion capture suit. The system offers a recording mode, which guides the user through the collection of a dataset of gestures, and a recognition mode, capable of detecting the recorded actions in real time. Gestures are recognized by training Support Vector Machines directly within our plugin. We demonstrate the effectiveness of our application through an experimental evaluation on a newly collected dataset. Furthermore, external applications can exploit NeuronUnityIntgration2.0's recognition capabilities thanks to a set of exposed API.

In this demo we present a system for immersive experiences in museums using Voice Commands (VCs) and Virtual Reality (VR). The system has been specifically designed for use by people with motor disabilities. Natural interaction is provided through Automatic Speech Recognition (ASR) and allows to experience VR environments wearing an Head Mounted Display (HMD), i.e. the Oculus Rift. Insights gathered during the implementation and results from an initial usability evaluation are reported.

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).

In this paper we propose a method for video recommendation in Social Networks based on crowdsourced and automatic video annotations of salient frames. We show how two human factors, users' self-expression in user profiles and perception of visual saliency in videos, can be exploited in order to stimulate annotations and to obtain an efficient representation of video content features. Results are assessed through experiments conducted on a prototype of social network for video sharing. Several baseline approaches are evaluated and we show how the proposed method improves over them.

In this demo we present smArt, a low-cost framework to quickly set up indoor exhibits featuring a smart navigation system for museums. The framework is web-based and allows the design on a digital map of a sensorized museum environment and the dynamic and assisted definition of the multimedia materials and sensors associated to the artworks. The knowledge-base uses semantic technologies and it is exploited by museum visitors to get directions and to have multimedia insights in a natural way. Indoor localisation and routing is provided taking advantage of active and passive sensors advertisements and user interactions. In this way we overcome the Global Positioning System (GPS) unavailability issue in indoor environments.

In this demo we present PITAGORA\footnote{Demo video available at http://bit.ly/1GgtUrN}: a mobile web contextual social network designed for the check-in area of an airport. The app provides recommendation of potential friends, local experts and targeted services. Recommendation is hybrid and combines social media analysis and collaborative filtering techniques. Users' recommendation has been evaluated through a user study with good results.

In this paper we present a system for content-based video recommendation that exploits visual saliency to better represent video features and content\footnote{Demo video available at http://bit.ly/1FYloeQ}. Visual saliency is used to select relevant frames to be presented in a web-based interface to tag and annotate video frames in a social network; it is also employed to summarize video content to create a more effective video representation used in the recommender system. The system exploits automatic annotations from CNN-based classifiers on salient frames and user generated annotations. We evaluate several baseline approaches and show how the proposed method improves over them.

In this paper we present Loki+Lire, a framework for the creation of web-based interfaces for search, annotation and presentation of multimedia data. The framework provides tools to ingest, transcode, present, annotate and index different types of media such as images, videos, audio files and textual documents. The front-end is compliant with the latest HTML5 standards, while the back-end allows system administrators to create processing pipelines that can be adapted for different tasks and purposes.

Two important challenges for social networks are the creation of targeted and personalized content for their users, selecting the most interesting material from the huge amount of user-generated content, and keeping user engagement , e.g. through creation and curation of users' profiles. In this demo we show a system for video commenting, sharing and interest discovery that combines recommendation algorithms, clustering techniques, tools for video tagging and evaluation of semantic resources relatedness. Combining these tools and techniques it becomes possible to provide personalized multimedia services and to improve and propagate interests and inter-personal connections through the network.

In this demo we present Flarty, a mobile location-based social network for the dynamic construction and recommendation of art routes in the city of Florence, Italy, via item based similarity algorithms, places topic extraction and user interest modeling. To achieve this goal Flarty derives knowledge from users check-ins and combines clustering techniques and recommendation algorithms, as well as features such as geo-location, to define groups of similar artworks or POIs (Points Of Interest) and to compute the most efficient routes likely to meet user's interests. Model analysis takes into account ratings, topics extracted from textual features associated with the POIs, and users preferences computed exploiting collaborative filtering techniques on their past behavior.

This demo presents a system based on social relationships, social knowledge and automatic video and textual content analysis for the discovery of videos in social networks. The system, developed as a web application, allows users to annotate, manually and automatically, and comment video frames and scenes enriching their content with tags, references to Facebook users and pages and Wikipedia resources. These annotations are used to semantically model the profile of each user extracting and expanding his interests and folksonomy, as well as resources of interest in his social graph. The automatically generated profile page is used to suggest to users new resources, Facebook friends and videos whose content is related to their interests and allows profile curation. A screencast showing an example of these functionalities is publicly available at: http://vimeo.com/miccunifi/facetube

We present a work-in-progress interactive exhibit for the mu- seum of Onna (L'Aquila, Italy). The Onna Onlus and the citizens of Onna have the ambitious project to rebuild the town affected by the earthquake of April 2009 and to create a museum in memory of Onna. In addition to the tradi- tional fruition tools for museums, we have been asked for an interactive system capable to communicate the past events and the efforts invested. We are working on a multi-modal system composed of an indoor environment in which visitors can interact with a natural interface system and an outdoor module based on a cross-platform mobile application. A pro- filing method is also exploited in order to extract a profile of interest of each visitor and then use it to suggest in-depth personalized and geo-located information about the disaster and the local history via multimedia contents.

In this paper we present a complete system for semantic and syn tactic annotation, browsing and search of multimedia data, that is based on a service oriented architecture, with web-based interfaces developed following the Rich Internet Application paradigm. The system has been designed to be: i) flexible and extendable, allowing users to select only the services they need or to add their own tools to the multimedia processing pipelines; ii) distributed, with services that can be executed in a cloud computing infrastructure and accessed through web applications; Hi) user-friendly, with interfaces that have a uniform interface on every platform and that have an interaction level similar to that of desktop applications. Extensive user trials in real-world setup, performed by archive and broadcaster professionals, have shown the efficacy and usability of the proposed solution.

In this paper we present a work-in-progress interactive exhibit for the museum of Onna, a little town near to L'Aquila (Italy), almost completely destroyed by the earthquake of April 2009. The installation will be developed as an environment in which visitors of the museum can interact with a natural interaction system and then discover the history of the disaster via rich multimedia contents. Visitors are detected through the adoption of an RFID-based technology, which allows to store their interaction history and build an interest profile used to enrich the experience. Different scenarios have been implemented and tested in order to evaluate the effectiveness of the proposed solution.

One of the most interesting issues in the field of cultural heritage is the adoption of multimedia systems for the visualization and organization of information. In this paper we present a natural interaction based system designed to represent multimedia contents related to the archaeological site of Shawbak, situated in the Petra region of Jordan. Contents are composed of texts, images and videos showing and explaining the archeological site areas and the history of the castle. This system was installed at the Limonaia di Palazzo Pitti (Italy) for the archeological exhibition called "From Petra to Shawbak".

In this technical demonstration we show an integrated web system for video search and annotation based on ontologies. The system is composed by three components: the Orione ontology-based search engine, the Sirio\footnote{Sirio was the hound of Orione. It was a dog so swift that no prey could escape it.} search interface, and the Pan web-based video annotation tool. The system is currently being developed within the EU IM3I project. The goal of the system is to provide an integrated environment for video annotation and retrieval of videos, for both technical and non-technical users. In fact, the search engine has different interfaces that permit different query modalities: free-text, natural language, graphical composition of concepts using Boolean and temporal relations and query by visual example. In addition, the ontology structure is exploited to encode semantic relations between concepts permitting, for example, to expand queries to synonyms and concept specializations. The annotation tool can be used to create ground-truth annotations to train automatic annotations systems, or to complement the results of automatic annotation, e.g. adding geolocalized information.

In this technical demonstration we present a novel web-based tool that allows a user friendly semantic browsing of video collections, based on ontologies, concepts, concept relations and concept clouds. The system is developed as a Rich Internet Application (RIA) to achieve a fast responsiveness and ease of use that can not be obtained by other web application paradigms, and uses streaming to access and inspect the videos. Users can also use the tool to browse the content of social and media sharing sites like YouTube, Flickr and Twitter, accessing these external resources through the ontologies used in the system. The tool has won the second prize in the Adobe YouGC contest, in the RIA category.

This paper addresses the design and development of MediaPick [1], an interactive multi-touch system for semantic search of multimedia contents. Our solution provides an intuitive, easy-to-use way to select concepts organized according to an ontological structure and retrieve the related contents. Users are then able to examine and organize such results through semantic or subjective criteria. As use case we considered professionals who work with huge multimedia archives (journalists, archivists, editors etc.).

In this technical demonstration we show the current version of Arneb, a web-based system for manual annotation of videos, developed within the EU VidiVideo project. This tool has been developed with the aim of creating ground truth annotations, that can be used for training and evaluating automatic video annotation systems. Annotations can be exported to MPEG-7 and OWL ontologies. The system has been developed according to the Rich Internet Application paradigm, allowing collaborative web-based annotation.

In this technical demonstration we show a web video search engine based on ontologies, the Sirio system, that has been developed within the EU VidiVideo project. The goal of the system is to provide a search engine for videos for both technical and non-technical users. In fact, the system has different interfaces that permit different query modalities: free-text, natural language, graphical composition of concepts using boolean and temporal relations and query by visual example. In addition, the ontology structure is exploited to encode semantic relations between concepts permitting, for example, to expand queries to synonyms and concept specializations.