Abstract
On this paper we use a newly-published method, DFFT, to estimate counts of crowds on unseen environments. Our main objective is to explore the relationship between noise in the input snapshots (of crowds) that a DFFT-based pipeline requires with the errors made on the predictions. If such a relationship exists we could apply our pipeline to the understanding of crowds; this application is extremely important to our industrial partners, that see utility in predicting crowds for objectives such as security of spatial planning. Our explorations indicate the possibility of such a characterization, but it depends on features of the actual environment being studied. Here we present 2 simulated environments of different difficulty, and we show how the predictions DFFT issues are of varying quality. We discuss the reasons we hypothesize are behind these performances and we set the ground for further experiments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Arthur, W.B.: Inductive reasoning and bounded rationality. Am. Econ. Rev. 84(2), 406–411 (1994)
Axelrod, R.: The Complexity of Cooperation: Agent-Based Models of Competition and Collaboration. Princeton University Press, Princeton (1997)
Fogel, D., Chellapilla, K., Angeline, P.: Inductive reasoning and bounded rationality reconsidered. IEEE Trans. Evol. Comput. 3(2), 142–146 (1999)
Méndez-Valderrama, J.F., Kinkhabwala, Y.A., Silver, J., Cohen, I., Arias, T.: Density-functional fluctuation theory of crowds. Nat. Commun. 9(3538) (2018). https://doi.org/10.1038/s41467-018-05750-z
Rand, W., Wilensky, U.: NetLogo El Farol model. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1997). http://ccl.northwestern.edu/netlogo/models/ElFarol
Vishwanath Sindagi, V.M.P.: CNN-based cascaded multi-task learning of high-level prior and density estimation for crowd counting. CoRR abs/1707.09605 (2017). http://arxiv.org/abs/1707.09605
Wilensky, U.: NetLogo. Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999). http://ccl.northwestern.edu/netlogo/
Wilensky, U.: An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo. The MIT Press, Cambridge (2015)
Acknowledgements
We thank our colleagues from the CRIM who provided insight and expertise that greatly assisted the research, and we appreciate the comments made by 3 anonymous reviewers on an earlier version of the manuscript. This work was partially supported by the MEI (Ministère de l’Économie et Innovation) of the Government of Québec.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Da Costa, L., Rajotte, JF. (2019). Crowd Prediction Under Uncertainty. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-18305-9_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-18304-2
Online ISBN: 978-3-030-18305-9
eBook Packages: Computer ScienceComputer Science (R0)