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AuthorKhan, Muhammad Asif
AuthorMenouar, Hamid
AuthorHamila, Ridha
Available date2023-04-04T09:09:09Z
Publication Date2023
Publication NameDigest of Technical Papers - IEEE International Conference on Consumer Electronics
ResourceScopus
URIhttp://dx.doi.org/10.1109/ICCE56470.2023.10043547
URIhttp://hdl.handle.net/10576/41637
AbstractDensity estimation is one of the most widely used method for crowd counting in which a deep learning model learns from head annotated crowd images to estimate crowd density in unseen images. Typically, the learning performance of the model is highly impacted by the accuracy of the annotations and inaccurate annotations may lead to localization and counting errors during prediction. A significant amount of works exist on crowd counting using perfectly labelled datasets but none of these explore the impact of annotation errors on the model accuracy. In this paper, we investigate the impact of imperfect labels (both noisy and missing labels) on crowd counting accuracy. We propose a system that automatically generate imperfect labels using a deep learning model (called annotator) which are then used to train a new crowd counting model (target model). Our analysis on two crowd counting models and two benchmark datasets shows that the proposed scheme achieves accuracy closer to that of the model trained with perfect labels showing robustness of crowd models to annotation errors. 2023 IEEE.
SponsorThis publication was made possible by the PDRA award PDRA7-0606-21012 from the Qatar National Research Fund (a member of The Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherIEEE
Subjectannotations
Crowd counting
density estimation
federated learning
imperfect labels
noisy data
TitleCrowd Density Estimation using Imperfect Labels
TypeConference
Volume Number2023-January
dc.accessType Abstract Only


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