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Efficient Temporal Consistency for Streaming Video Scene Analysis

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F13%3A00212529" target="_blank" >RIV/68407700:21230/13:00212529 - isvavai.cz</a>

  • Result on the web

    <a href="http://dx.doi.org/10.1109/ICRA.2013.6630567" target="_blank" >http://dx.doi.org/10.1109/ICRA.2013.6630567</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/ICRA.2013.6630567" target="_blank" >10.1109/ICRA.2013.6630567</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Efficient Temporal Consistency for Streaming Video Scene Analysis

  • Original language description

    We address the problem of image-based scene analysis from streaming video, as would be seen from a moving platform, in order to efficiently generate spatially and temporally consistent predictions of semantic categories over time. In contrast to previoustechniques which typically address this problem in batch and/or through graphical models, we demonstrate that by learning visual similarities between pixels across frames, a simple filtering algorithm is able to achieve high performance predictions in an efficient and online/causal manner. Our technique is a meta-algorithm that can be efficiently wrapped around any scene analysis technique that produces a per-pixel semantic category distribution.We validate our approach over three different scene analysis techniques on three different datasets that contain different semantic object categories. Our experiments demonstrate that our approach is very efficient in practice and substantially improves the consistency of the predictions over t

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    JD - Use of computers, robotics and its application

  • OECD FORD branch

Result continuities

  • Project

    <a href="/en/project/GBP103%2F12%2FG084" target="_blank" >GBP103/12/G084: Center for Large Scale Multi-modal Data Interpretation</a><br>

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Others

  • Publication year

    2013

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    ICRA2013: Proceedings of 2013 IEEE International Conference on Robotics and Automation

  • ISBN

    978-1-4673-5641-1

  • ISSN

    1050-4729

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    133-139

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    Karlsruhe

  • Event date

    May 6, 2013

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article