3D-TV Production from Conventional Cameras for Sports Broadcast

3DTV production of live sports events presents a challenging problem involving conflicting requirements of main- taining broadcast stereo picture quality with practical problems in developing robust systems for cost effective deployment. In this paper we propose an alternative approach to stereo production in sports events using the conventional monocular broadcast cameras for 3D reconstruction of the event and subsequent stereo rendering. This approach has the potential advantage over stereo camera rigs of recovering full scene depth, allowing inter-ocular distance and convergence to be adapted according to the requirements of the target display and enabling stereo coverage from both existing and ‘virtual’ camera positions without additional cameras. A prototype system is presented with results of sports TV production trials for rendering of stereo and free-viewpoint video sequences of soccer and rugby

Real-time 3D human body pose estimation from monocular RGB input

Human motion capture finds extensive application in movies, games, sports and biomechanical analysis. However, existing motion capture solutions require cumbersome external and/or on-body instrumentation, or use active sensors with limits on the possible capture volume dictated by power consumption. The ubiquity and ease of deployment of RGB cameras makes monocular RGB based human motion capture an extremely useful problem to solve, which would lower the barrier-to entry for content creators to employ motion capture tools, and enable newer applications of human motion capture. This thesis demonstrates the first real-time monocular RGB based motion-capture solutions that work in general scene settings. They are based on developing neural network based approaches to address the ill-posed problem of estimating 3D human pose from a single RGB image, in combination with model based fitting. In particular, the contributions of this work make advances towards three key aspects of real-time monocular RGB based motion capture, namely speed, accuracy, and the ability to work for general scenes. New training datasets are proposed, for single-person and multi-person scenarios, which, together with the proposed transfer learning based training pipeline, allow learning based approaches to be appearance invariant. The training datasets are accompanied by evaluation benchmarks with multiple avenues of fine-grained evaluation. The evaluation benchmarks differ visually from the training datasets, so as to promote efforts towards solutions that generalize to in-the-wild scenes. The proposed task formulations for the single-person and multi-person case allow higher accuracy, and incorporate additional qualities such as occlusion robustness, that are helpful in the context of a full motion capture solution. The multi-person formulations are designed to have a nearly constant inference time regardless of the number of subjects in the scene, and combined with contributions towards fast neural network inference, enable real-time 3D pose estimation for multiple subjects. Combining the proposed learning-based approaches with a model-based kinematic skeleton fitting step provides temporally stable joint angle estimates, which can be readily employed for driving virtual characters.Menschlicher Motion Capture findet umfangreiche Anwendung in Filmen, Spielen, Sport und biomechanischen Analysen. Bestehende Motion-Capture-Lösungen erfordern jedoch umständliche externe Instrumentierung und / oder Instrumentierung am Körper, oder verwenden aktive Sensoren deren begrenztes Erfassungsvolumen durch den Stromverbrauch begrenzt wird. Die Allgegenwart und einfache Bereitstellung von RGB-Kameras macht die monokulare RGB-basierte Motion Capture zu einem äußerst nützlichen Problem. Dies würde die Eintrittsbarriere für Inhaltsersteller für die Verwendung der Motion Capture verringern und neuere Anwendungen dieser Tools zur Analyse menschlicher Bewegungen ermöglichen. Diese Arbeit zeigt die ersten monokularen RGB-basierten Motion-Capture-Lösungen in Echtzeit, die in allgemeinen Szeneneinstellungen funktionieren. Sie basieren auf der Entwicklung neuronaler netzwerkbasierter Ansätze, um das schlecht gestellte Problem der Schätzung der menschlichen 3D-Pose aus einem einzelnen RGB-Bild in Kombination mit einer modellbasierten Anpassung anzugehen. Insbesondere machen die Beiträge dieser Arbeit Fortschritte in Richtung drei Schlüsselaspekte der monokularen RGB-basierten Echtzeit-Bewegungserfassung, nämlich Geschwindigkeit, Genauigkeit und die Fähigkeit, für allgemeine Szenen zu arbeiten. Es werden neue Trainingsdatensätze für Einzel- und Mehrpersonen-Szenarien vorgeschlagen, die zusammen mit der vorgeschlagenen Trainingspipeline, die auf Transferlernen basiert, ermöglichen, dass lernbasierte Ansätze nicht von Unterschieden im Erscheinungsbild des Bildes beeinflusst werden. Die Trainingsdatensätze werden von Bewertungsbenchmarks mit mehreren Möglichkeiten einer feinkörnigen Bewertung begleitet. Die angegebenen Benchmarks unterscheiden sich visuell von den Trainingsaufzeichnungen, um die Entwicklung von Lösungen zu fördern, die sich auf verschiedene Szenen verallgemeinern lassen. Die vorgeschlagenen Aufgabenformulierungen für den Einzel- und Mehrpersonenfall ermöglichen eine höhere Genauigkeit und enthalten zusätzliche Eigenschaften wie die Robustheit der Okklusion, die im Kontext einer vollständigen Bewegungserfassungslösung hilfreich sind. Die Mehrpersonenformulierungen sind so konzipiert, dass sie unabhängig von der Anzahl der Subjekte in der Szene eine nahezu konstante Inferenzzeit haben. In Kombination mit Beiträgen zur schnellen Inferenz neuronaler Netze ermöglichen sie eine 3D-Posenschätzung in Echtzeit für mehrere Subjekte. Die Kombination der vorgeschlagenen lernbasierten Ansätze mit einem modellbasierten kinematischen Skelettanpassungsschritt liefert zeitlich stabile Gelenkwinkelschätzungen, die leicht zum Ansteuern virtueller Charaktere verwendet werden können

Videos in Context for Telecommunication and Spatial Browsing

The research presented in this thesis explores the use of videos embedded in panoramic imagery to transmit spatial and temporal information describing remote environments and their dynamics. Virtual environments (VEs) through which users can explore remote locations are rapidly emerging as a popular medium of presence and remote collaboration. However, capturing visual representation of locations to be used in VEs is usually a tedious process that requires either manual modelling of environments or the employment of specific hardware. Capturing environment dynamics is not straightforward either, and it is usually performed through specific tracking hardware. Similarly, browsing large unstructured video-collections with available tools is difficult, as the abundance of spatial and temporal information makes them hard to comprehend. At the same time, on a spectrum between 3D VEs and 2D images, panoramas lie in between, as they offer the same 2D images accessibility while preserving 3D virtual environments surrounding representation. For this reason, panoramas are an attractive basis for videoconferencing and browsing tools as they can relate several videos temporally and spatially. This research explores methods to acquire, fuse, render and stream data coming from heterogeneous cameras, with the help of panoramic imagery. Three distinct but interrelated questions are addressed. First, the thesis considers how spatially localised video can be used to increase the spatial information transmitted during video mediated communication, and if this improves quality of communication. Second, the research asks whether videos in panoramic context can be used to convey spatial and temporal information of a remote place and the dynamics within, and if this improves users' performance in tasks that require spatio-temporal thinking. Finally, the thesis considers whether there is an impact of display type on reasoning about events within videos in panoramic context. These research questions were investigated over three experiments, covering scenarios common to computer-supported cooperative work and video browsing. To support the investigation, two distinct video+context systems were developed. The first telecommunication experiment compared our videos in context interface with fully-panoramic video and conventional webcam video conferencing in an object placement scenario. The second experiment investigated the impact of videos in panoramic context on quality of spatio-temporal thinking during localization tasks. To support the experiment, a novel interface to video-collection in panoramic context was developed and compared with common video-browsing tools. The final experimental study investigated the impact of display type on reasoning about events. The study explored three adaptations of our video-collection interface to three display types. The overall conclusion is that videos in panoramic context offer a valid solution to spatio-temporal exploration of remote locations. Our approach presents a richer visual representation in terms of space and time than standard tools, showing that providing panoramic contexts to video collections makes spatio-temporal tasks easier. To this end, videos in context are suitable alternative to more difficult, and often expensive solutions. These findings are beneficial to many applications, including teleconferencing, virtual tourism and remote assistance

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity