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.
@article{arxiv.1803.03095,
title = {Leveraging Unlabeled Data for Crowd Counting by Learning to Rank},
author = {Xialei Liu and Joost van de Weijer and Andrew D. Bagdanov},
journal= {arXiv preprint arXiv:1803.03095},
year = {2018}
}