Analysis of spatio-temporal traffic patterns based on pedestrian trajectories

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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

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To cite the version in the repository, please use this identifier: https://doi.org/10.15488/697

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Sum total of downloads: 241




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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.
License of this version: CC BY 3.0
Document Type: article
Publishing status: publishedVersion
Issue Date: 2016
Appears in Collections:Fakultät für Bauingenieurwesen und Geodäsie

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downloads by country:

pos. country downloads
total perc.
1 image of flag of Germany Germany 127 52.70%
2 image of flag of China China 24 9.96%
3 image of flag of United States United States 23 9.54%
4 image of flag of United Kingdom United Kingdom 20 8.30%
5 image of flag of Korea, Republic of Korea, Republic of 5 2.07%
6 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 4 1.66%
7 image of flag of Netherlands Netherlands 3 1.24%
8 image of flag of India India 3 1.24%
9 image of flag of France France 3 1.24%
10 image of flag of Brazil Brazil 3 1.24%
    other countries 26 10.79%

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