Leveraging Unlabeled Data for Crowd Counting by Learning to Rank

Understand

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.