The DOI matches its Crossref record; 49 of the 49 references it cites were found in a registry or an index.
Deeply learned attributes for crowded scene understanding
Abstract
Crowded 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.
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Cites
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Locality-constrained Linear Coding for image classification
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Cited by
Cross-scene crowd counting via deep convolutional neural networks
Zhang, C., Li, H., Wang, X., & Yang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298684 · Cited by 1,121
Self-citationA Review of Human Activity Recognition Methods
Vrigkas, M., Nikou, C., & Kakadiaris, I. A. (2015) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00028 · Cited by 566
STCT: Sequentially Training Convolutional Networks for Visual Tracking
Wang, L., Ouyang, W., Wang, X., & Lu, H. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.153 · Cited by 258
Self-citationPhysics Inspired Methods for Crowd Video Surveillance and Analysis: A Survey
Zhang, X., Yu, Q., & Yu, H. (2018) · IEEE Access · doi:10.1109/access.2018.2878733 · Cited by 70
Metadata
- Authors
- Shao, J., Kang, K., Loy, C. C., & Wang, X.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 4657-4666
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1962468782
- Cited by
- 280
- References
- 49
- FWCI
- 14.96
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shao, J., Kang, K., Loy, C. C., & Wang, X. (2015). Deeply learned attributes for crowded scene understanding. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7299097
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