<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1761</id>
    <completedYear>2020</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2723</pageFirst>
    <pageLast>2730</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace>Canberra, Australia</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-01-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pose Based Action Recognition of Vulnerable Road Users Using Recurrent Neural Networks</title>
    <abstract language="eng">This work investigates the use of knowledge about three dimensional (3D) poses and Recurrent Neural Networks (RNNs) for detection of basic movements, such as wait, start, move, stop, turn left, turn right, and no turn, of pedestrians and cyclists in road traffic. The 3D poses model the posture of individual body parts of these vulnerable road users (VRUs). Fields of application for this technology are, for example, driver assistance systems or autonomous driving functions of vehicles. In road traffic, VRUs are often occluded and only become visible in the immediate vicinity of the vehicle. Hence, our proposed approach is able to classify basic movements after different and especially short observation periods. The classification will then be successively improved in case of a longer observation. This allows countermeasures, such as emergency braking, to be initiated early if necessary. The benefits of using 3D poses are evaluated by a comparison with a method based solely on the head trajectory. We also investigate the effects of different observation periods. Overall, knowledge about 3D poses improves the basic movement detection, in particular for short observation periods. The greatest improvements are achieved for the basic movements start, stop, turn left, and turn right.</abstract>
    <parentTitle language="eng">2020 IEEE Symposium Series on Computational Intelligence (SSCI)</parentTitle>
    <identifier type="isbn">978-1-7281-2548-0</identifier>
    <identifier type="doi">10.1109/SSCI47803.2020.9308462</identifier>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Keine Lizenz - es gilt das deutsche Urheberrecht</licence>
    <author>Viktor Kress</author>
    <author>Steven Schreck</author>
    <author>Stefan Zernetsch</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>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fußgänger</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Fahrrad</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>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>2475</id>
    <completedYear>2022</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>265</pageFirst>
    <pageLast>272</pageLast>
    <pageNumber>8</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">A Holistic View on Probabilistic Trajectory Forecasting – Case Study. Cyclist Intention Detection</title>
    <parentTitle language="eng">2022 IEEE Intelligent Vehicles Symposium (IV)</parentTitle>
    <identifier type="doi">https://doi.org/10.1109/IV51971.2022.9827220</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T15:09:29Z","timestamp":1725808169747},"reference-count":24,"publisher":"IEEE","license":[{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,6,5]],"date-time":"2022-06-05T00:00:00Z","timestamp":1654387200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100004326","name":"Bayer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100004326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,6,5]]},"DOI":"10.1109\/iv51971.2022.9827220","type":"proceedings-article","created":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T19:33:28Z","timestamp":1658259208000},"page":"265-272","source":"Crossref","is-referenced-by-count":5,"title":["A Holistic View on Probabilistic Trajectory Forecasting \u2013 Case Study. Cyclist Intention Detection"],"prefix":"10.1109","author":[{"given":"Stefan","family":"Zernetsch","sequence":"first","affiliation":[{"name":"University of Applied Sciences Aschaffenburg,Faculty of Engineering,Aschaffenburg,Germany"}]},{"given":"Hannes","family":"Reichert","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Aschaffenburg,Faculty of Engineering,Aschaffenburg,Germany"}]},{"given":"Viktor","family":"Kress","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Aschaffenburg,Faculty of Engineering,Aschaffenburg,Germany"}]},{"given":"Konrad","family":"Doll","sequence":"additional","affiliation":[{"name":"University of Applied Sciences Aschaffenburg,Faculty of Engineering,Aschaffenburg,Germany"}]},{"given":"Bernhard","family":"Sick","sequence":"additional","affiliation":[{"name":"University of Kassel,Intelligent Embedded Systems Lab,Kassel,Germany"}]}],"member":"263","reference":[{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569434"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813889"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814258"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1002\/we.230"},{"year":"2020","author":"zernetsch","key":"ref17"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2012.6338672"},{"key":"ref19","first-page":"67","article-title":"Detecting intentions of vulnerable road users based on collective intelligence","author":"bieshaar","year":"2017","journal-title":"AAET - Automatisiertes und Vernetztes Fahren"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2016.7535484"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/IntelliSys.2015.7361171"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2923319"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2018.2873900"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2017.8317691"},{"key":"ref7","first-page":"2317","article-title":"Stereo-Vision-Based Pedestrian&amp;#x2019;s Intention Detection in a Moving Vehicle","author":"k\u00f6hler","year":"2015","journal-title":"2015 IEEE 18th International Conference on Intelligent Transportation Systems"},{"key":"ref2","first-page":"1","article-title":"Pedestrian Path, Pose, and Intention Prediction Through Gaussian Process Dynamical Models and Pedestrian Activity Recognition","author":"minguez","year":"2018","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR48806.2021.9413233"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2017.8285249"},{"article-title":"Cyclist Actions: Optical Flow Sequences and Trajectories","year":"2020","author":"zernetsch","key":"ref20"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1006\/jcph.1995.1209"},{"journal-title":"Machine Learning A Probabilistic Perspective","year":"2","author":"murphy","key":"ref21"},{"key":"ref24","article-title":"Quantile surfaces &amp;#x2013; generalizing quantile regression to multivariate targets","author":"bieshaar","year":"2020","journal-title":"arXiv 2010 05898"},{"key":"ref23","first-page":"1050","article-title":"Dropout as a bayesian approximation: Representing model uncertainty in deep learning","author":"gal","year":"2016","journal-title":"Proceedings of the 33rd International Conference on International Conference on Machine Learning - Volume 48 ser ICML&amp;#x2019;16 JMLR org"}],"event":{"name":"2022 IEEE Intelligent Vehicles Symposium (IV)","start":{"date-parts":[[2022,6,4]]},"location":"Aachen, Germany","end":{"date-parts":[[2022,6,9]]}},"container-title":["2022 IEEE Intelligent Vehicles Symposium (IV)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9826996\/9826997\/09827220.pdf?arnumber=9827220","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,8]],"date-time":"2022-08-08T20:05:07Z","timestamp":1659989107000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9827220\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,5]]},"references-count":24,"URL":"https:\/\/doi.org\/10.1109\/iv51971.2022.9827220","relation":{},"subject":[],"published":{"date-parts":[[2022,6,5]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://doi.org/10.15223/policy-029</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="HAB_Review">ja</enrichment>
    <enrichment key="conference_title">2022 IEEE Intelligent Vehicles Symposium (IV)</enrichment>
    <enrichment key="conference_place">Aachen, Germany</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Stefan Zernetsch</author>
    <author>Hannes Reichert</author>
    <author>Viktor Kress</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="institutes" number="">Kooperative automatisierte Verkehrssysteme</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
  </doc>
  <doc>
    <id>2635</id>
    <completedYear>2023</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2592</pageFirst>
    <pageLast>2603</pageLast>
    <pageNumber>12</pageNumber>
    <edition/>
    <issue>3</issue>
    <volume>8</volume>
    <type>article</type>
    <publisherName>Institute of Electrical and Electronics Engineers (IEEE)</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users’ Trajectories</title>
    <parentTitle language="eng">IEEE Transactions on Intelligent Vehicles</parentTitle>
    <identifier type="issn">2379-8904</identifier>
    <identifier type="doi">10.1109/TIV.2022.3149624</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,16]],"date-time":"2025-04-16T05:39:18Z","timestamp":1744781958507,"version":"3.37.3"},"reference-count":31,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T00:00:00Z","timestamp":1677628800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"name":"Zentrum Digitalisierung.Bayern"},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["SPP 1835"],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Kooperativ interagierende Automobile","award":["DO 1186\/1-2","SI 674\/11-2"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Intell. Veh."],"published-print":{"date-parts":[[2023,3]]},"DOI":"10.1109\/tiv.2022.3149624","type":"journal-article","created":{"date-parts":[[2022,2,8]],"date-time":"2022-02-08T20:40:15Z","timestamp":1644352815000},"page":"2592-2603","source":"Crossref","is-referenced-by-count":5,"title":["Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users\u2019 Trajectories"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9314-2424","authenticated-orcid":false,"given":"Viktor","family":"Kress","sequence":"first","affiliation":[{"name":"Faculty of Engineering, University of Applied Sciences Aschaffen- burg, Aschaffenburg, Germany"}]},{"given":"Fabian","family":"Jeske","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Applied Sciences Aschaffen- burg, Aschaffenburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2016-5059","authenticated-orcid":false,"given":"Stefan","family":"Zernetsch","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Applied Sciences Aschaffen- burg, Aschaffenburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3746-2319","authenticated-orcid":false,"given":"Konrad","family":"Doll","sequence":"additional","affiliation":[{"name":"Faculty of Engineering, University of Applied Sciences Aschaffen- burg, Aschaffenburg, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9467-656X","authenticated-orcid":false,"given":"Bernhard","family":"Sick","sequence":"additional","affiliation":[{"name":"Intelligent Embedded Systems Lab, University of Kassel, Kassel, Germany"}]}],"member":"263","reference":[{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2836305"},{"key":"ref12","first-page":"407","article-title":"Discrete residual flow for probabilistic pedestrian behavior prediction","volume":"100","author":"jain","year":"0","journal-title":"Proc Conf Robot Learn"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/WACV.2018.00135"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI44817.2019.9003023"},{"key":"ref31","doi-asserted-by":"publisher","DOI":"10.1109\/WACV45572.2020.9093519"},{"key":"ref30","first-page":"1321","article-title":"On calibration of modern neural networks","author":"guo","year":"0","journal-title":"Proc Int Conf Mach Learn"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2018.8500562"},{"article-title":"Quantile surfaces - Generalizing quantile regression to multivariate targets","year":"2020","author":"bieshaar","key":"ref10"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2280766"},{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2017.8285249"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1109\/LRA.2020.2974393"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC45102.2020.9294296"},{"article-title":"Pedestrians and cyclists in road traffic: Trajectories, 3D poses and semantic maps","year":"0","author":"kress","key":"ref19"},{"article-title":"Mixture density networks","year":"1994","author":"bishop","key":"ref18"},{"key":"ref24","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"ronneberger","year":"2015","journal-title":"Proc Int Conf Med Image Comput Computer-Assisted Intervention"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.006"},{"key":"ref26","first-page":"2901","article-title":"Obtaining well calibrated probabilities using Bayesian binning","author":"naeini","year":"0","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"ref25","first-page":"802","article-title":"Convolutional LSTM network: A machine learning approach for precipitation nowcasting","author":"shi","year":"0","journal-title":"Proc Int Conf Neural Inf Process"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.143"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI.2018.8628660"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.603"},{"key":"ref28","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1175\/1520-0450(1973)012&lt;0595:ANVPOT&gt;2.0.CO;2"},{"key":"ref29","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2018.8569434"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8813889"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814258"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00240"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2019.2923319"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00717"}],"container-title":["IEEE Transactions on Intelligent Vehicles"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/7274857\/10109992\/09707640.pdf?arnumber=9707640","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T18:52:11Z","timestamp":1687805531000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9707640\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3]]},"references-count":31,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/tiv.2022.3149624","relation":{},"ISSN":["2379-8904","2379-8858"],"issn-type":[{"type":"electronic","value":"2379-8904"},{"type":"print","value":"2379-8858"}],"subject":[],"published":{"date-parts":[[2023,3]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">journal-article</enrichment>
    <enrichment key="local_crossrefLicence">https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorIdentifierOrcid_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorIdentifierOrcid_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorIdentifierOrcid_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,PersonAuthorIdentifierOrcid_5,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,Issue,Volume,CompletedYear,IdentifierIssn,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Viktor Kress</author>
    <author>Fabian Jeske</author>
    <author>Stefan Zernetsch</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>Autonomes Fahrzeug</value>
    </subject>
    <collection role="institutes" number="">Kooperative automatisierte Verkehrssysteme</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
  </doc>
  <doc>
    <id>2637</id>
    <completedYear>2021</completedYear>
    <publishedYear/>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>7</pageLast>
    <pageNumber>7</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information</title>
    <parentTitle language="eng">2021 IEEE International Smart Cities Conference (ISC2)</parentTitle>
    <identifier type="doi">10.1109/ISC253183.2021.9562857</identifier>
    <enrichment key="opus_doi_flag">true</enrichment>
    <enrichment key="opus_import_data">{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T01:11:14Z","timestamp":1740100274522,"version":"3.37.3"},"reference-count":23,"publisher":"IEEE","license":[{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,9,7]],"date-time":"2021-09-07T00:00:00Z","timestamp":1630972800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["1835"],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,9,7]]},"DOI":"10.1109\/isc253183.2021.9562857","type":"proceedings-article","created":{"date-parts":[[2021,10,16]],"date-time":"2021-10-16T10:05:05Z","timestamp":1634378705000},"page":"1-7","source":"Crossref","is-referenced-by-count":4,"title":["Cyclist Trajectory Forecasts by Incorporation of Multi-View Video Information"],"prefix":"10.1109","author":[{"given":"Stefan","family":"Zernetsch","sequence":"first","affiliation":[]},{"given":"Oliver","family":"Trupp","sequence":"additional","affiliation":[]},{"given":"Viktor","family":"Kress","sequence":"additional","affiliation":[]},{"given":"Konrad","family":"Doll","sequence":"additional","affiliation":[]},{"given":"Bernhard","family":"Sick","sequence":"additional","affiliation":[]}],"member":"263","reference":[{"key":"ref10","article-title":"The Kinetics Human Action Video Dataset","volume":"abs 1705 6950","author":"kay","year":"2017","journal-title":"ar Xiv"},{"key":"ref11","article-title":"UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild","volume":"abs 1212 402","author":"soomro","year":"2012","journal-title":"CoRR"},{"key":"ref12","article-title":"Image sequence based cyclist action recognition using multi-stream 3d convolution","author":"zernetsch","year":"2020","journal-title":"2020 International Conference on Pattern Recognition (ICPR) (accepted)"},{"journal-title":"Cyclist Actions Optical Flow Sequences and Trajectories","year":"2020","author":"zernetsch","key":"ref13"},{"year":"2020","author":"zernetsch","key":"ref14"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1109\/ITSC.2012.6338672"},{"key":"ref16","article-title":"Speed\/accuracy trade-offs for modern convolutional object detectors","volume":"abs 1611 10012","author":"huang","year":"2016","journal-title":"CoRR"},{"key":"ref17","first-page":"740","author":"lin","year":"2014","journal-title":"Microsoft coco Common objects in context"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00931"},{"key":"ref19","first-page":"267","author":"bar-shalom","year":"2001","journal-title":"Estimation for kinematic models"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2017.7995734"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.110"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/SSCI47803.2020.9308462"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2018.2836305"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00144"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2013.2280766"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2016.7535484"},{"journal-title":"Selbstlernende Algorithmen zur videobasierten Ab-sichtserkennung von Fu&amp;#x00DF;g&amp;#x00E4;ngern","year":"2016","author":"goldhammer","key":"ref1"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.502"},{"journal-title":"TensorFlow Large-Scale Machine Learning on Heterogeneous Systems","year":"2015","author":"abadi","key":"ref20"},{"key":"ref22","first-page":"1","article-title":"Statistical comparisons of classifiers over multiple data sets","volume":"7","author":"demsar","year":"2006","journal-title":"J Mach Learn Res"},{"journal-title":"Nonparametric Statistical Methods Wiley Series in Probability and Statistics","year":"2013","author":"hollander","key":"ref21"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1109\/IVS.2019.8814258"}],"event":{"name":"2021 IEEE International Smart Cities Conference (ISC2)","start":{"date-parts":[[2021,9,7]]},"location":"Manchester, United Kingdom","end":{"date-parts":[[2021,9,10]]}},"container-title":["2021 IEEE International Smart Cities Conference (ISC2)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9562741\/9562768\/09562857.pdf?arnumber=9562857","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T15:47:22Z","timestamp":1652197642000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9562857\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,7]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/isc253183.2021.9562857","relation":{},"subject":[],"published":{"date-parts":[[2021,9,7]]}}}</enrichment>
    <enrichment key="local_crossrefDocumentType">proceedings-article</enrichment>
    <enrichment key="local_crossrefLicence">https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html</enrichment>
    <enrichment key="local_import_origin">crossref</enrichment>
    <enrichment key="local_doiImportPopulated">PersonAuthorFirstName_1,PersonAuthorLastName_1,PersonAuthorFirstName_2,PersonAuthorLastName_2,PersonAuthorFirstName_3,PersonAuthorLastName_3,PersonAuthorFirstName_4,PersonAuthorLastName_4,PersonAuthorFirstName_5,PersonAuthorLastName_5,Enrichmentconference_title,Enrichmentconference_place,PublisherName,TitleMain_1,TitleParent_1,PageNumber,PageFirst,PageLast,CompletedYear,Enrichmentlocal_crossrefLicence</enrichment>
    <enrichment key="conference_title">2021 IEEE International Smart Cities Conference (ISC2)</enrichment>
    <enrichment key="conference_place">Manchester, United Kingdom</enrichment>
    <enrichment key="copyright">1</enrichment>
    <enrichment key="opus.source">doi-import</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">false</enrichment>
    <author>Stefan Zernetsch</author>
    <author>Oliver Trupp</author>
    <author>Viktor Kress</author>
    <author>Konrad Doll</author>
    <author>Bernhard Sick</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Autonomes Fahrzeug</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Radfahrer</value>
    </subject>
    <collection role="institutes" number="">Kooperative automatisierte Verkehrssysteme</collection>
    <collection role="forschungsschwerpunkte" number="">Intelligent Mobility</collection>
  </doc>
</export-example>
