Development of a realistic crowd simulation environment for fine-grained validation of people tracking methods
Foszner P., Szczesna A., Ciampi L., Messina N., Cygan A., Bizon B., Cogiel M., Golba D., Macioszek E., Staniszewski M.
Crowd simulation Realism enhancement People and car simulation People tracking Deep Learning
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.
Source: VISIGRAPP 2023 - 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 222–229, Lisbon, Portugal, 19-21/02/2023
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@inproceedings{oai:it.cnr:prodotti:479243,
title = {Development of a realistic crowd simulation environment for fine-grained validation of people tracking methods},
author = {Foszner P. and Szczesna A. and Ciampi L. and Messina N. and Cygan A. and Bizon B. and Cogiel M. and Golba D. and Macioszek E. and Staniszewski M.},
doi = {10.5220/0011691500003417},
booktitle = {VISIGRAPP 2023 - 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, pp. 222–229, Lisbon, Portugal, 19-21/02/2023},
year = {2023}
}
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