Analysis of spatio-temporal traffic patterns based on pedestrian trajectories

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dc.identifier.uri http://dx.doi.org/10.15488/697
dc.identifier.uri http://www.repo.uni-hannover.de/handle/123456789/721
dc.contributor.author Busch, S.
dc.contributor.author Schindler, T.
dc.contributor.author Klinger, Tobias
dc.contributor.author Brenner, Claus
dc.date.accessioned 2016-11-21T07:54:40Z
dc.date.available 2016-11-21T07:54:40Z
dc.date.issued 2016
dc.identifier.citation Busch, S.; Schindler, T.; Klinger, T.; Brenner, C.: Analysis of spatio-temporal traffic patterns based on pedestrian trajectories. In: International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016), S. 497-503. DOI: http://dx.doi.org/10.5194/isprsarchives-XLI-B2-497-2016
dc.description.abstract For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation. eng
dc.language.iso eng
dc.publisher Göttingen : Copernicus GmbH
dc.relation.ispartof 23rd International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences Congress, ISPRS 2016, 12–19 July 2016, Prague, Czech Republic
dc.relation.ispartofseries International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives 41 (2016)
dc.rights CC BY 3.0
dc.rights.uri http://creativecommons.org/licenses/by/3.0/
dc.subject Dynamic prior map eng
dc.subject Pedestrian behaviour prediction eng
dc.subject Periodic event analysis eng
dc.subject Traffic pattern eng
dc.subject Walking path network eng
dc.subject Automobile drivers eng
dc.subject Complex networks eng
dc.subject Remote sensing eng
dc.subject Video recording eng
dc.subject Dynamic priors eng
dc.subject Event analysis eng
dc.subject Pedestrian movement eng
dc.subject Pedestrian trajectories eng
dc.subject Public transportation eng
dc.subject Traffic pattern eng
dc.subject Trajectory segments eng
dc.subject Walking paths eng
dc.subject Trajectories eng
dc.subject.ddc 500 | Naturwissenschaften ger
dc.subject.ddc 520 | Astronomie, Kartographie ger
dc.title Analysis of spatio-temporal traffic patterns based on pedestrian trajectories
dc.type article
dc.type conferenceObject
dc.type Text
dc.relation.issn 1682-1750
dc.relation.doi http://dx.doi.org/10.5194/isprsarchives-XLI-B2-497-2016
dc.bibliographicCitation.volume 41
dc.bibliographicCitation.firstPage 497
dc.bibliographicCitation.lastPage 503
dc.description.version publishedVersion
tib.accessRights frei zug�nglich


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