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AuthorIdrees H.
AuthorTayyab M.
AuthorAthrey K.
AuthorZhang D.
AuthorAl-Maadeed, Somaya
AuthorRajpoot N.
AuthorShah M.
Available date2022-05-19T10:23:12Z
Publication Date2018
Publication NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ResourceScopus
Identifierhttp://dx.doi.org/10.1007/978-3-030-01216-8_33
URIhttp://hdl.handle.net/10576/31132
AbstractWith multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals; visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNNs, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes.
SponsorAcknowledgment. This work was made possible in part by NPRP grant number NPRP 7-1711-1-312 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.
Languageen
PublisherSpringer Verlag
SubjectArtificial intelligence
Computer science
Computers
Convolution neural network
Crowd analysis
Crowd counting
Evaluation measures
High quality images
Localization
Loss functions
State of the art
Computer vision
TitleComposition Loss for Counting, Density Map Estimation and Localization in Dense Crowds
TypeConference Paper
Pagination544-559
Volume Number11206 LNCS
dc.accessType Abstract Only


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