loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Paweł Foszner 1 ; Agnieszka Szczęsna 1 ; Luca Ciampi 2 ; Nicola Messina 2 ; Adam Cygan 3 ; Bartosz Bizoń 3 ; Michał Cogiel 4 ; Dominik Golba 4 ; Elżbieta Macioszek 5 and Michał Staniszewski 1

Affiliations: 1 Department of Computer Graphics, Vision and Digital Systems, Faculty of Automatic Control, Electronics and Computer Science, Silesian University of Technology, Akademicka 2A, 44-100 Gliwice, Poland ; 2 Institute of Information Science and Technologies, National Research Council, Via G. Moruzzi 1, 56124 Pisa, Italy ; 3 QSystems.pro sp. z o.o. Mochnackiego 34, 41-907 Bytom, Poland ; 4 Blees sp. z o.o. Zygmunta Starego 24a/10, 44-100 Gliwice, Poland ; 5 Department of Transport Systems, Traffic Engineering and Logistics, Faculty of Transport and Aviation Engineering, Silesian University of Technology, Krasińskiego 8, 40-019 Katowice, Poland

Keyword(s): Crowd Simulation, Realism Enhancement, People, Car Simulation, People Tracking, Deep Learning.

Abstract: Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or other sensors). The use of simulation tools can significantly reduce the time required to generate scenario-specific crowd datasets, facilitate data-driven research, and next build functional machine learning models. The main goal of this work was to develop an extension of crowd simulation (named CrowdSim2) and prove its usability in the application of people-tracking algorithms. The simulator is developed using the very popular Unity 3D engine with particular emphasis on the aspects of realism in the environment, weather conditions, traffic, and the movement and models of individual agents. Finally, three methods of tracking were used to validate generated dataset: IOU-Tracker, Deep-Sort, and Deep-TAMA.

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 18.97.14.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Foszner, P. ; Szczęsna, A. ; Ciampi, L. ; Messina, N. ; Cygan, A. ; Bizoń, B. ; Cogiel, M. ; Golba, D. ; Macioszek, E. and Staniszewski, M. (2023). Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods. In Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP; ISBN 978-989-758-634-7; ISSN 2184-4321, SciTePress, pages 222-229. DOI: 10.5220/0011691500003417

@conference{grapp23,
author={Paweł Foszner and Agnieszka Szczęsna and Luca Ciampi and Nicola Messina and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Elżbieta Macioszek and Michał Staniszewski},
title={Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods},
booktitle={Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP},
year={2023},
pages={222-229},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011691500003417},
isbn={978-989-758-634-7},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2023) - GRAPP
TI - Development of a Realistic Crowd Simulation Environment for Fine-Grained Validation of People Tracking Methods
SN - 978-989-758-634-7
IS - 2184-4321
AU - Foszner, P.
AU - Szczęsna, A.
AU - Ciampi, L.
AU - Messina, N.
AU - Cygan, A.
AU - Bizoń, B.
AU - Cogiel, M.
AU - Golba, D.
AU - Macioszek, E.
AU - Staniszewski, M.
PY - 2023
SP - 222
EP - 229
DO - 10.5220/0011691500003417
PB - SciTePress