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.