The crowd as a cameraman : on-stage display of crowdsourced mobile video at large-scale events

Recording videos with smartphones at large-scale events such as concerts and festivals is very common nowadays. These videos register the atmosphere of the event as it is experienced by the crowd and offer a perspective that is hard to capture by the professional cameras installed throughout the venue. In this article, we present a framework to collect videos from smartphones in the public and blend these into a mosaic that can be readily mixed with professional camera footage and shown on displays during the event. The video upload is prioritized by matching requests of the event director with video metadata, while taking into account the available wireless network capacity. The proposed framework's main novelty is its scalability, supporting the real-time transmission, processing and display of videos recorded by hundreds of simultaneous users in ultra-dense Wi-Fi environments, as well as its proven integration in commercial production environments. The framework has been extensively validated in a controlled lab setting with up to 1 000 clients as well as in a field trial where 1 183 videos were collected from 135 participants recruited from an audience of 8 050 people. 90 % of those videos were uploaded within 6.8 minutes

“AccessBIM” - A Model of Environmental Characteristics for Vision Impaired Indoor Navigation and Way Finding

The complexity of modern indoor environments has made navigation difficult for individuals with vision impairment. Hence, this thesis presents the AccessBIM framework, which is an optimized database that’s facilitates generation of a real-time floor plan with path determination. The AccessBIM framework has the potential to play an integral role in improving the independence and quality of life for people with vision impairment whilst also decreasing the cost to the community related to caretakers

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Soundscape Generation Using Web Audio Archives

Os grandes e crescentes acervos de áudio na web têm transformado a prática do design de som. Neste contexto, sampling -- uma ferramenta essencial do design de som -- mudou de gravações mecânicas para os domínios da cópia e reprodução no computador. A navegação eficaz nos grandes acervos e a recuperação de conteúdo tornaram-se um problema bem identificado em Music Information Retrieval, nomeadamente através da adoção de metodologias baseadas no conteúdo do áudio.Apesar da sua robustez e eficácia, as soluções tecnológicas atuais assentam principalmente em métodos (estatísticos) de processamento de sinal, cuja terminologia atinge um nível de adequação centrada no utilizador.Esta dissertação avança uma nova estratégia orientada semanticamente para navegação e recuperação de conteúdo de áudio, em particular, sons ambientais, a partir de grandes acervos de áudio na web. Por fim, pretendemos simplificar a extração de pedidos definidos pelo utilizador para promover uma geração fluida de paisagens sonoras. No nosso trabalho, os pedidos aos acervos de áudio na web são feitos por dimensões afetivas que se relacionam com estados emocionais (exemplo: baixa ativação e baixa valência) e descrições semânticas das fontes de áudio (exemplo: chuva). Para tal, mapeamos as anotações humanas das dimensões afetivas para descrições espectrais de áudio extraídas do conteúdo do sinal. A extração de novos sons dos acervos da web é feita estipulando um pedido que combina um ponto num plano afetivo bidimensional e tags semânticas. A aplicação protótipo, MScaper, implementa o método no ambiente Ableton Live. A avaliação da nossa pesquisa avaliou a confiabilidade perceptual dos descritores espectrais de áudio na captura de dimensões afetivas e a usabilidade da MScaper. Os resultados mostram que as características espectrais do áudio capturam significativamente as dimensões afetivas e que o MScaper foi entendido pelos os utilizadores experientes como tendo excelente usabilidade.The large and growing archives of audio content on the web have been transforming the sound design practice. In this context, sampling -- a fundamental sound design tool -- has shifted from mechanical recording to the realms of the copying and cutting on the computer. To effectively browse these large archives and retrieve content became a well-identified problem in Music Information Retrieval, namely through the adoption of audio content-based methodologies. Despite its robustness and effectiveness, current technological solutions rely mostly on (statistical) signal processing methods, whose terminology do attain a level of user-centered explanatory adequacy.This dissertation advances a novel semantically-oriented strategy for browsing and retrieving audio content, in particular, environmental sounds, from large web audio archives. Ultimately, we aim to streamline the retrieval of user-defined queries to foster a fluid generation of soundscapes. In our work, querying web audio archives is done by affective dimensions that relate to emotional states (e.g., low arousal and low valence) and semantic audio source descriptions (e.g., rain). To this end, we map human annotations of affective dimensions to spectral audio-content descriptions extracted from the signal content. Retrieving new sounds from web archives is then made by specifying a query which combines a point in a 2-dimensional affective plane and semantic tags. A prototype application, MScaper, implements the method in the Ableton Live environment. An evaluation of our research assesses the perceptual soundness of the spectral audio-content descriptors in capturing affective dimensions and the usability of MScaper. The results show that spectral audio features significantly capture affective dimensions and that MScaper has been perceived by expert-users as having excellent usability

FoP: Never-Ending Learner for Multimedia Knowledge Extraction

International audience—In this paper we present our system Faces of Politics (henceforth FoP), that is able to continuously learn multimedia knowledge of Web multimedia resources about the presence of person(s) in a pictures and to leverage this knowledge to the Linked Open Data cloud (LOD-cloud). FoP promotes both scalability of the data lift process for this domain and a structured knowledge representation for complex queries. The system was bootstraped using Freebase data about politicians and their pictures, and we show that the model provides a good generalization with an error rate below 7%. Meantime, FoP not only relates a person to a multimedia resource, but it also detects and publishes metadata on the position of the person in the picture. Moreover, it supports the presence of several persons in the picture. At this step, FoP is also giving data in return to the LoD cloud that fed him in the first place: it leverages Linked Data on people recognized in these pictures, and on which rectangle area. This allows fine-grained queries like creating a curation of documents in which a person is depicted relatively to another for instance. On a technical point-of-view, we also provide a Website for browsing FoP knowledge base as Web users, and we also offer a public SPARQL endpoint for robots or other Web applications

On Quantifying Qualitative Geospatial Data: A Probabilistic Approach

Living in the era of data deluge, we have witnessed a web content explosion, largely due to the massive availability of User-Generated Content (UGC). In this work, we specifically consider the problem of geospatial information extraction and representation, where one can exploit diverse sources of information (such as image and audio data, text data, etc), going beyond traditional volunteered geographic information. Our ambition is to include available narrative information in an effort to better explain geospatial relationships: with spatial reasoning being a basic form of human cognition, narratives expressing such experiences typically contain qualitative spatial data, i.e., spatial objects and spatial relationships. To this end, we formulate a quantitative approach for the representation of qualitative spatial relations extracted from UGC in the form of texts. The proposed method quantifies such relations based on multiple text observations. Such observations provide distance and orientation features which are utilized by a greedy Expectation Maximization-based (EM) algorithm to infer a probability distribution over predefined spatial relationships; the latter represent the quantified relationships under user-defined probabilistic assumptions. We evaluate the applicability and quality of the proposed approach using real UGC data originating from an actual travel blog text corpus. To verify the quality of the result, we generate grid-based maps visualizing the spatial extent of the various relations

A Survey of Volunteered Open Geo-Knowledge Bases in the Semantic Web

Over the past decade, rapid advances in web technologies, coupled with innovative models of spatial data collection and consumption, have generated a robust growth in geo-referenced information, resulting in spatial information overload. Increasing 'geographic intelligence' in traditional text-based information retrieval has become a prominent approach to respond to this issue and to fulfill users' spatial information needs. Numerous efforts in the Semantic Geospatial Web, Volunteered Geographic Information (VGI), and the Linking Open Data initiative have converged in a constellation of open knowledge bases, freely available online. In this article, we survey these open knowledge bases, focusing on their geospatial dimension. Particular attention is devoted to the crucial issue of the quality of geo-knowledge bases, as well as of crowdsourced data. A new knowledge base, the OpenStreetMap Semantic Network, is outlined as our contribution to this area. Research directions in information integration and Geographic Information Retrieval (GIR) are then reviewed, with a critical discussion of their current limitations and future prospects

Integrating Spatial Data Infrastructures (SDIs) with Volunteered Geographic Information (VGI) creating a Global GIS platform

Spatial Data Infrastructures (SDIs) are a special category of data hubs that involve technological and human resources and follow well defined legal and technical procedures to collect, store, manage and distribute spatial data. INSPIRE is the EU’s authoritative SDI in which each Member State provides access to their spatial data across a wide spectrum of data themes to support policy-making. In contrast, Volunteered Geographic Information (VGI) is one type of user-generated geographic information (GI) where volunteers use the web and mobile devices to create, assemble and disseminate spatial information. There are similarities and differences between SDIs and VGI, as well as advantages and disadvantages to both. Thus, the integration of these two data sources will enhance what is offered to end users to facilitate decision-making. This idea of integration is in its early stages, because several key issues need to be considered and resolved first. Therefore, this chapter discusses the challenges of integrating VGI with INSPIRE and outlines a generic framework for a global integrated GIS platform, similar in concept to Digital Earth and Virtual Geographic Environments (VGEs), as a realistic scenario for advancements in the short term