This title appears in the Scientific Report :
2015
Please use the identifier:
http://dx.doi.org/10.1007/978-3-319-20904-3_40 in citations.
Robust Marker-Based Tracking for Measuring Crowd Dynamics
Robust Marker-Based Tracking for Measuring Crowd Dynamics
We present a system to conduct laboratory experiments with thousands of pedestrians. Each participant is equipped with an individual marker to enable us to perform precise tracking and identification. We propose a novel rotation invariant marker design which guarantees a minimal Hamming distance bet...
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Personal Name(s): | Mehner, Wolfgang (Corresponding author) |
---|---|
Boltes, Maik / Mathias, Markus / Leibe, Bastian | |
Contributing Institute: |
Jülich Supercomputing Center; JSC |
Published in: |
Computer Vision Systems |
Imprint: |
Cham
Springer International Publishing
2015
|
Physical Description: |
445 - 455 |
ISBN: |
978-3-319-20903-6 (print) 978-3-319-20904-3 (electronic) |
DOI: |
10.1007/978-3-319-20904-3_40 |
Conference: | 10th International Conference on Computer Vision Systems, Copenhagen (Denmark), 2015-07-06 - 2015-07-09 |
Document Type: |
Contribution to a conference proceedings |
Research Program: |
Computational Science and Mathematical Methods |
Series Title: |
Lecture Notes in Computer Science
9163 |
Publikationsportal JuSER |
We present a system to conduct laboratory experiments with thousands of pedestrians. Each participant is equipped with an individual marker to enable us to perform precise tracking and identification. We propose a novel rotation invariant marker design which guarantees a minimal Hamming distance between all used codes. This increases the robustness of pedestrian identification. We present an algorithm to detect these markers, and to track them through a camera network. With our system we are able to capture the movement of the participants in great detail, resulting in precise trajectories for thousands of pedestrians. The acquired data is of great interest in the field of pedestrian dynamics. It can also potentially help to improve multi-target tracking approaches, by allowing better insights into the behaviour of crowds. |