Deeply learned attributes for crowded scene understanding
Refereed conference paper presented and published in conference proceedings


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AbstractCrowded scene understanding is a fundamental problem in computer vision. In this study, we develop a multi-task deep model to jointly learn and combine appearance and motion features for crowd understanding. We propose crowd motion channels as the input of the deep model and the channel design is inspired by generic properties of crowd systems. To well demonstrate our deep model, we construct a new large-scale WWW Crowd dataset with 10, 000 videos from 8, 257 crowded scenes, and build an attribute set with 94 attributes on WWW. We further measure user study performance on WWW and compare this with the proposed deep models. Extensive experiments show that our deep models display significant performance improvements in cross-scene attribute recognition compared to strong crowd-related feature-based baselines, and the deeply learned features behave a superior performance in multi-task learning.
All Author(s) ListShao J., Kang K., Loy C.C., Wang X.
Name of ConferenceIEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015
Start Date of Conference07/06/2015
End Date of Conference12/06/2015
Place of ConferenceBoston
Country/Region of ConferenceUnited States of America
Detailed descriptionorganized by IEEE Computer Society,
Year2015
Month10
Day14
Volume Number07-12-June-2015
Pages4657 - 4666
ISBN9781467369640
ISSN1063-6919
LanguagesEnglish-United Kingdom

Last updated on 2020-22-10 at 01:14