Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
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.
Cite
Text
Liu et al. "Leveraging Unlabeled Data for Crowd Counting by Learning to Rank." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018. doi:10.1109/CVPR.2018.00799Markdown
[Liu et al. "Leveraging Unlabeled Data for Crowd Counting by Learning to Rank." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018.](https://mlanthology.org/cvpr/2018/liu2018cvpr-leveraging/) doi:10.1109/CVPR.2018.00799BibTeX
@inproceedings{liu2018cvpr-leveraging,
title = {{Leveraging Unlabeled Data for Crowd Counting by Learning to Rank}},
author = {Liu, Xialei and van de Weijer, Joost and Bagdanov, Andrew D.},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2018},
doi = {10.1109/CVPR.2018.00799},
url = {https://mlanthology.org/cvpr/2018/liu2018cvpr-leveraging/}
}