Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
This addresses the data scarcity issue in crowd counting for applications like surveillance and event management, though it is an incremental improvement over existing methods.
The paper tackles the problem of limited labeled data for crowd counting by leveraging unlabeled crowd imagery through a learning-to-rank framework, achieving state-of-the-art results on two challenging datasets.
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.