Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images, we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons...

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Bibliographic Details
Published in2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition pp. 7661 - 7669
Main Authors Liu, Xialei, van de Weijer, Joost, Bagdanov, Andrew D.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.06.2018
Subjects
Computer vision
Context modeling
Estimation
Head
Task analysis
Training
Visualization
Online AccessGet full text
ISSN1063-6919
DOI10.1109/CVPR.2018.00799

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Summary:We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking of cropped images, we use the observation that any sub-image of a crowded scene image is guaranteed to contain the same number or fewer persons than the super-image. This allows us to address the problem of limited size of existing datasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and query-by-example image retrieval, respectively. We demonstrate how to efficiently learn from these unlabeled datasets by incorporating learning-to-rank in a multi-task network which simultaneously ranks images and estimates crowd density maps. Experiments on two of the most challenging crowd counting datasets show that our approach obtains state-of-the-art results.
ISSN:1063-6919
DOI:10.1109/CVPR.2018.00799