57,470 research outputs found

    XML-driven exploitation of combined scalability in scalable H.264/AVC bitstreams

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    The heterogeneity in the contemporary multimedia environments requires a format-agnostic adaptation framework for the consumption of digital video content. Scalable bitstreams can be used in order to satisfy as many circumstances as possible. In this paper, the scalable extension on the H.264/AVC specification is used to obtain the parent bitstreams. The adaptation along the combined scalability axis of the bitstreams is done in a format-independent manner. Therefore, an abstraction layer of the bitstream is needed. In this paper, XML descriptions are used representing the high-level structure of the bitstreams by relying on the MPEG-21 Bitstream Syntax Description Language standard. The exploitation of the combined scalability is executed in the XML domain by implementing the adaptation process in a Streaming Transformation for XML (STX) stylesheet. The algorithm used in the transformation of the XML description is discussed in detail in this paper. From the performance measurements, one can conclude that the STX transformation in the XML domain and the generation of the corresponding adapted bitstream can be realized in real time

    The development of a rich multimedia training environment for crisis management: using emotional affect to enhance learning

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    PANDORA is an EU FP7-funded project developing a novel training and learning environment for Gold Commanders, individuals who carry executive responsibility for the services and facilities identified as strategically critical e.g. Police, Fire, in crisis management strategic planning situations. A key part of the work for this project is considering the emotional and behavioural state of the trainees, and the creation of more realistic, and thereby stressful, representations of multimedia information to impact on the decision-making of those trainees. Existing training models are predominantly paper-based, table-top exercises, which require an exercise of imagination on the part of the trainees to consider not only the various aspects of a crisis situation but also the impacts of interventions, and remediating actions in the event of the failure of an intervention. However, existing computing models and tools are focused on supporting tactical and operational activities in crisis management, not strategic. Therefore, the PANDORA system will provide a rich multimedia information environment, to provide trainees with the detailed information they require to develop strategic plans to deal with a crisis scenario, and will then provide information on the impacts of the implementation of those plans and provide the opportunity for the trainees to revise and remediate those plans. Since this activity is invariably multi-agency, the training environment must support group-based strategic planning activities and trainees will occupy specific roles within the crisis scenario. The system will also provide a range of non-playing characters (NPC) representing domain experts, high-level controllers (e.g. politicians, ministers), low-level controllers (tactical and operational commanders), and missing trainee roles, to ensure a fully populated scenario can be realised in each instantiation. Within the environment, the emotional and behavioural state of the trainees will be monitored, and interventions, in the form of environmental information controls and mechanisms impacting on the stress levels and decisionmaking capabilities of the trainees, will be used to personalise the training environment. This approach enables a richer and more realistic representation of the crisis scenario to be enacted, leading to better strategic plans and providing trainees with structured feedback on their performance under stress

    Interaction Issues in Computer Aided Semantic\ud Annotation of Multimedia

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    The CASAM project aims to provide a tool for more efficient and effective annotation of multimedia documents through collaboration between a user and a system performing an automated analysis of the media content. A critical part of the project is to develop a user interface which best supports both the user and the system through optimal human-computer interaction. In this paper we discuss the work undertaken, the proposed user interface and underlying interaction issues which drove its development

    Activity-driven content adaptation for effective video summarisation

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    In this paper, we present a novel method for content adaptation and video summarization fully implemented in compressed-domain. Firstly, summarization of generic videos is modeled as the process of extracted human objects under various activities/events. Accordingly, frames are classified into five categories via fuzzy decision including shot changes (cut and gradual transitions), motion activities (camera motion and object motion) and others by using two inter-frame measurements. Secondly, human objects are detected using Haar-like features. With the detected human objects and attained frame categories, activity levels for each frame are determined to adapt with video contents. Continuous frames belonging to same category are grouped to form one activity entry as content of interest (COI) which will convert the original video into a series of activities. An overall adjustable quota is used to control the size of generated summarization for efficient streaming purpose. Upon this quota, the frames selected for summarization are determined by evenly sampling the accumulated activity levels for content adaptation. Quantitative evaluations have proved the effectiveness and efficiency of our proposed approach, which provides a more flexible and general solution for this topic as domain-specific tasks such as accurate recognition of objects can be avoided
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