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.

Accepted by CVPR18

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Cited by in corpus (8)

• Crowd Counting and Density Estimation by Trellis Encoder-Decoder Network
• Learning from Synthetic Data for Crowd Counting in the Wild
• Perspective-Guided Convolution Networks for Crowd Counting
• Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
• Feature-aware Adaptation and Density Alignment for Crowd Counting in Video Surveillance
• NAS-Count: Counting-by-Density with Neural Architecture Search
• PCC Net: Perspective Crowd Counting via Spatial Convolutional Network
• On Attention Modules for Audio-Visual Synchronization