The DOI matches its Crossref record; 41 of the 41 references it cites were found in a registry or an index.
Scene-Independent Group Profiling in Crowd
Abstract
Groups are the primary entities that make up a crowd. Understanding group-level dynamics and properties is thus scientifically important and practically useful in a wide range of applications, especially for crowd understanding. In this study we show that fundamental group-level properties, such as intra-group stability and inter-group conflict, can be systematically quantified by visual descriptors. This is made possible through learning a novel Collective Transition prior, which leads to a robust approach for group segregation in public spaces. From the prior, we further devise a rich set of group property visual descriptors. These descriptors are scene-independent, and can be effectively applied to public-scene with variety of crowd densities and distributions. Extensive experiments on hundreds of public scene video clips demonstrate that such property descriptors are not only useful but also necessary for group state analysis and crowd scene understanding.
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Cites
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You'll never walk alone: Modeling social behavior for multi-target tracking
Pellegrini, S., Ess, A., Schindler, K., & van Gool, L. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459260 · Cited by 1,683 · cited 1×
“Most existing imagery-based crowd analysis methods tend to treat a crowd either as a collection of individuals [10, 23, 27] or as an aggregated whole [3, 8, 24, 17].”
BackgroundAbnormal crowd behavior detection using social force model
Mehran, R., Oyama, A., & Shah, M. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206641 · Cited by 1,672
The Walking Behaviour of Pedestrian Social Groups and Its Impact on Crowd Dynamics
Moussaïd, M., Perozo, N., Garnier, S., Helbing, D., & Theraulaz, G. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010047 · Cited by 1,120
Hydrodynamics and phases of flocks
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Cited by
Detecting Coherent Groups in Crowd Scenes by Multiview Clustering
Wang, Q., Chen, M., Nie, F., & Li, X. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2875002 · Cited by 299
Deeply learned attributes for crowded scene understanding
Shao, J., Kang, K., Loy, C. C., & Wang, X. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299097 · Cited by 280
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., Loy, C. C., & Wang, X.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 2227-2234
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2042015300
- Cited by
- 254
- References
- 41
- FWCI
- 18.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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