60 research outputs found

    So what can we actually do with content-based video retrieval?

    Get PDF
    In this talk I will give a roller-coaster survey of the state of the art in automatic video analysis, indexing, summarisation, search and browsing as demonstrated in the annual TRECVid benchmarking evaluation campaign. I will concentrate on content-based techniques for video management which form a complement to the dominant paradigm of metadata or tag-based video management and I will use example techniques to illustrate these

    Video-4-Video: using video for searching, classifying and summarising video

    Get PDF
    YouTube has meant that we are now becoming accustomed to searching for video clips, and finding them, for both work and leisure pursuits. But YouTube, like the Internet Archive, OpenVideo and almost everything other video library, doesn't use video to find video, it uses metadata, usually based on user generated content (UGC). But what if we don't know what we're looking for and the metadata doesn't help, or we have poor metadata or no UGC, can we use the video to find video ? Can we automatically derive semantic concepts directly from video which we can use for retrieval or summarisation ? Many dozens of research groups throughout the world work on the problems associated with content-based video search, content-based detection of semantic concepts, shot boundary detection, content-based summarisation and content-based event detection. In this presentation we give a summary of the achievements of almost a decade of research by the TRECVid community, including a report on performance of groups in different TRECVid tasks. We present the modus operandi of the annual TRECVid benchmarking, the problems associated with running an annual evaluation for nearly 100 research groups every year and an overview of the most successful approaches to each task

    Video, semantics and the sensor web

    Get PDF
    This talk will present a snapshot of some of the current projects underway in the CLARITY centre which contribute to the proposition of the sensor web. In particular we focus on lifelogging, tennis, cycling and environmental water quality monitoring as examples of sensor webs. The then present a summary of approaches taken to identifying the presence or absence of groups of semantic features, in video. The annual TRECVid activity has been benchmarking the effectiveness of various approaches since 2001 and we will examine what is the performance of these detectors, what are the trends in this area, and what is the state of the art. We will discover that the performance of individual detectors varies widely depending on the nature of the semantic feature, the quality of training data and its dependence on other detectors. There is a strong parallel between this and the way that sensors (environmental, physiological, etc.) which make up the sensor web, can also have poor accuracy levels when used in isolation but whose individual performances can be improved when used in combination

    TRECVID 2014 -- An Overview of the Goals, Tasks, Data, Evaluation Mechanisms and Metrics

    No full text
    International audienceThe TREC Video Retrieval Evaluation (TRECVID) 2014 was a TREC-style video analysis and retrieval evaluation, the goal of which remains to promote progress in content-based exploitation of digital video via open, metrics-based evaluation. Over the last dozen years this effort has yielded a better under- standing of how systems can effectively accomplish such processing and how one can reliably benchmark their performance. TRECVID is funded by the NIST with support from other US government agencies. Many organizations and individuals worldwide contribute significant time and effort

    Review of Person Re-identification Techniques

    Full text link
    Person re-identification across different surveillance cameras with disjoint fields of view has become one of the most interesting and challenging subjects in the area of intelligent video surveillance. Although several methods have been developed and proposed, certain limitations and unresolved issues remain. In all of the existing re-identification approaches, feature vectors are extracted from segmented still images or video frames. Different similarity or dissimilarity measures have been applied to these vectors. Some methods have used simple constant metrics, whereas others have utilised models to obtain optimised metrics. Some have created models based on local colour or texture information, and others have built models based on the gait of people. In general, the main objective of all these approaches is to achieve a higher-accuracy rate and lowercomputational costs. This study summarises several developments in recent literature and discusses the various available methods used in person re-identification. Specifically, their advantages and disadvantages are mentioned and compared.Comment: Published 201
    corecore