| Home | << 1 >> |
|
| Record | Links | |||||
|---|---|---|---|---|---|---|
Author  |
Xialei Liu; Joost Van de Weijer; Andrew Bagdanov |
 
 |
||||
| Title | Leveraging Unlabeled Data for Crowd Counting by Learning to Rank | Type | Conference Article | |||
| Year | 2018 | Publication | 31st IEEE Conference on Computer Vision and Pattern Recognition | Abbreviated Journal | ||
| Volume | Issue | Pages | 7661 - 7669 | |||
| Keywords | Task analysis; Training; Computer vision; Visualization; Estimation; Head; Context modeling | |||||
| 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 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. |
|||||
| Address | Salt Lake City; USA; June 2018 | |||||
| Corporate Author | Thesis | |||||
| Publisher | Place of Publication | Editor | ||||
| Language | Summary Language | Original Title | ||||
| Series Editor | Series Title | Abbreviated Series Title | ||||
| Series Volume | Series Issue | Edition | ||||
| ISSN | ISBN | Medium | ||||
| Area | Expedition | Conference | CVPR | |||
| Notes | LAMP; 600.109; 600.106; 600.120;CIC | Approved | no | |||
| Call Number | Admin @ si @ LWB2018 | Serial | 3159 | |||
| Permanent link to this record | ||||||