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A classification-regression deep learning model for people counting

Xu, Bolei; Zou, Wenbin; Garibaldi, Jonathan; Qiu, Guoping

Authors

Bolei Xu

Wenbin Zou

GUOPING QIU GUOPING.QIU@NOTTINGHAM.AC.UK
Professor of Visual Information Processing



Contributors

Kohei Arai
Editor

Supriya Kapoor
Editor

Rahul Bhatia
Editor

Abstract

In this paper, we construct a multi-task deep learning model to simultaneously predict people number and the level of crowd density. Motivated by the success of applying " ambiguous labelling " to age estimation problem, we also manage to employ this strategy to the people counting problem. We show that it is a reasonable strategy since people counting problem is similar to the age estimation problem. Also, by applying " ambiguous labelling " , we are able to augment the size of training dataset, which is a desirable property when applying to deep learning model. In a series of experiment, we show that the " ambiguous labelling " strategy can not only improve the performance of deep learning but also enhance the prediction ability of traditional computer vision methods such as Random Projection Forest with hand-crafted features.

Citation

Xu, B., Zou, W., Garibaldi, J., & Qiu, G. (2018). A classification-regression deep learning model for people counting. In K. Arai, S. Kapoor, & R. Bhatia (Eds.), Intelligent Systems and Applications Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1 (136-149). https://doi.org/10.1007/978-3-030-01054-6_9

Conference Name Intelligent Systems Conference 2018 (IntelliSys 2018)
Conference Location London, UK
Start Date Sep 6, 2018
End Date Sep 7, 2018
Acceptance Date Sep 1, 2018
Online Publication Date Nov 8, 2018
Publication Date Nov 9, 2018
Deposit Date Dec 13, 2018
Publicly Available Date Mar 29, 2024
Journal Intelligent Systems Conference
Publisher Springer Publishing Company
Pages 136-149
Series Title Advances in Intelligent Systems and Computing
Series Number 868
Series ISSN 2194-5365
Book Title Intelligent Systems and Applications Proceedings of the 2018 Intelligent Systems Conference (IntelliSys) Volume 1
Chapter Number 9
ISBN 978-3-030-01053-9
DOI https://doi.org/10.1007/978-3-030-01054-6_9
Keywords People counting; Deep learning; Ambiguous la- belling
Public URL https://nottingham-repository.worktribe.com/output/1412867
Publisher URL https://link.springer.com/chapter/10.1007/978-3-030-01054-6_9

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