TY  - CONF
AU  - Xialei Liu
AU  - Joost Van de Weijer
AU  - Andrew Bagdanov
A2  - CVPR
PY  - 2018//
TI  - Leveraging Unlabeled Data for Crowd Counting by Learning to Rank
BT  - 31st IEEE Conference on Computer Vision and Pattern Recognition
SP  - 7661
EP  - 7669
KW  - Task analysis
KW  - Training
KW  - Computer vision
KW  - Visualization
KW  - Estimation
KW  - Head
KW  - Context modeling
N2  - We propose a novel crowd counting approach that leverages abundantly available unlabeled crowd imagery in a learning-to-rank framework. To induce a ranking ofcropped 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 existingdatasets for crowd counting. We collect two crowd scene datasets from Google using keyword searches and queryby-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-ofthe-art results.
L1  - http://refbase.cvc.uab.es/files/LWB2018.pdf
UR  - http://dx.doi.org/10.1109/CVPR.2018.00799
N1  - LAMP; 600.109; 600.106; 600.120
ID  - Xialei Liu2018
ER  -