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  <doc>
    <id>1911</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-01-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Image Sequence Based Cyclist Action Recognition Using Multi-Stream 3D Convolution</title>
    <abstract language="eng">In this article, we present an approach to detect basic movements of cyclists in real world traffic situations based on image sequences, optical flow (OF) sequences, and past positions using a multi-stream 3D convolutional neural network (3D-ConvNet) architecture. To resolve occlusions of cyclists by other traffic participants or road structures, we use a wide angle stereo camera system mounted at a heavily frequented public intersection. We created a large dataset consisting of 1,639 video sequences containing cyclists, recorded in real world traffic, resulting in over 1.1 million samples. Through modeling the cyclists' behavior by a state machine of basic cyclist movements, our approach takes every situation into account and is not limited to certain scenarios. We compare our method to an approach solely based on position sequences. Both methods are evaluated taking into account frame wise and scene wise classification results of basic movements, and detection times of basic movement transitions, where our approach outperforms the position based approach by producing more reliable detections with shorter detection times. Our code and parts of our dataset are made publicly available.</abstract>
    <parentTitle language="eng">2020 25th International Conference on Pattern Recognition (ICPR)</parentTitle>
    <identifier type="url">https://ieeexplore.ieee.org/document/9413233</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Stefan Zernetsch</author>
    <author>Steven Schreck</author>
    <author>Viktor Kress</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
  <doc>
    <id>1818</id>
    <completedYear>2017</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>deu</language>
    <pageFirst>82</pageFirst>
    <pageLast>82</pageLast>
    <pageNumber/>
    <edition/>
    <issue>03</issue>
    <volume>25</volume>
    <type>article</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-03-01</completedDate>
    <publishedDate>2017-03-01</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="deu">Sicher unterwegs - Fortschritte beim aktiven Fußgängerschutz</title>
    <parentTitle language="deu">messtec drives Automation</parentTitle>
    <identifier type="url">https://www.wileyindustrynews.com/restricted-files/161702</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Klaus Zindler</author>
    <editor>Verlag WILEY-VCH</editor>
    <author>Konrad Doll</author>
    <author>Bertold Huber</author>
    <subject>
      <language>deu</language>
      <type>uncontrolled</type>
      <value>aktiver Fußgängerschutz, automatische Brems- und Ausweichmanöver</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrerassistenzsystem</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <collection role="forschungsschwerpunkte" number="">Artifical Intelligence and Data Science</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
    <collection role="forschungsschwerpunkte" number="">Robotics and Automation</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Sensors and Signals</collection>
  </doc>
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