Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

Xialei Liu, Joost van de Weijer, Andrew D. Bagdanov; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 7661-7669

Abstract


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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Liu_2018_CVPR,
author = {Liu, Xialei and van de Weijer, Joost and Bagdanov, Andrew D.},
title = {Leveraging Unlabeled Data for Crowd Counting by Learning to Rank},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2018}
}