CrowdSim2 is an extension of crowd simulation tool designed in Unity for the purpose of generation massive synthetic data. Such a generated data from crowd simulation enables to validate various methods in terms of tracking multiple people and detect objects (in that example pedestrians and cars). Information summarizing number of folders, seconds and frames of data for different weather conditions Condition Folders Seconds Frames Sun 2899 86 970 2 174 250 Rain 1633 48 990 1 224 750 Fog 1653 49 590 1 239 750 Snow 1646 49 380 1 234 500 Due to the limitations of the Zenodo platform, we could only include part of data here. If you are interested in the entire collection - please visit the project website: CrowdSim Acknowledgments This work was supported by: European Union funds awarded to Blees Sp. z o.o. under grant POIR.01.01.01-00-0952/20-00 “Development of a system for analysing vision data captured by public transport vehicles interior monitoring, aimed at detecting undesirable situations/behaviours and passenger counting (including their classification by age group) and the objects they carry”); EC H2020 project “AI4media: a Centre of Excellence delivering next generation AI Research and Training at the service of Media, Society and Democracy” under GA 951911; research project (RAU-6, 2020) and projects for young scientists of the Silesian University of Technology (Gliwice, Poland); research project INAROS (INtelligenza ARtificiale per il mOnitoraggio e Supporto agli anziani), Tuscany POR FSE CUP B53D21008060008. Publication supported under the Excellence Initiative - Research University program implemented at the Silesian University of Technology, year 2022. This research was supported by the European Union from the European Social Fund in the framework of the project ”Silesian University of Technology as a Center of Modern Education based on research and innovation” POWR.03.05.00- 00-Z098/17 We are thankful for students participating in design of Crowd Simulator: Piotr Bartosz, Stanisław Wróbel, Marcin Wola, Angelika Gluch and Marek Matuszczyk. Citing the Crowdsim 2 The Crowdsim 2 is released under a Creative Commons Attribution license, so please cite the Crowdsim 2 if it is used in your work in any form. Published academic papers should use the academic paper citation for our Crowdsim 2 paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people TBA: Article citations using this dataset will appear here and this Zenodo Dataset @dataset{crowdsim2_zenodo, title={Crowd simulation (CrowdSim2) for tracking and object detection}, DOI={10.5281/zenodo.7262220}, publisher={Zenodo}, author={Agnieszka Szczęsna and Paweł Foszner and Adam Cygan and Bartosz Bizoń and Michał Cogiel and Dominik Golba and Luca Ciampi and Nicola Messina and Elżbieta Macioszek and Michał Staniszewski}, year={2023}, month={Feb} }