The DOI matches its Crossref record; 43 of the 43 references it cites were found in a registry or an index.
Detecting Coherent Groups in Crowd Scenes by Multiview Clustering
Abstract
Detecting coherent groups is fundamentally important for crowd behavior analysis. In the past few decades, plenty of works have been conducted on this topic, but most of them have limitations due to the insufficient utilization of crowd properties and the arbitrary processing of individuals. In this study, a Multiview-based Parameter Free framework (MPF) is proposed. Based on the L1-norm and L2-norm, we design two versions of the multiview clustering method, which is the main part of the proposed framework. This paper presents the contributions on three aspects: (1) a new structural context descriptor is designed to characterize the structural properties of individuals in crowd scenes; (2) a self-weighted multiview clustering method is proposed to cluster feature points by incorporating their orientation and context similarities; and (3) a novel framework is introduced for group detection, which is able to determine the group number automatically without any parameter or threshold to be tuned. The effectiveness of the proposed framework is evaluated on real-world crowd videos, and the experimental results show its promising performance on group detection. In addition, the proposed multiview clustering method is also evaluated on a synthetic dataset and several standard benchmarks, and its superiority over the state-of-the-art competitors is demonstrated.
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Citations
Cites
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,758
Depth-First Search and Linear Graph Algorithms
Tarjan, R. (1972) · SIAM Journal on Computing · doi:10.1137/0201010 · Cited by 6,081
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Single-Image Crowd Counting via Multi-Column Convolutional Neural Network
Zhang, Y., Zhou, D., Chen, S., Gao, S., & Ma, Y. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.70 · Cited by 2,241
Ballerini, M., Cabibbo, N., Candelier, R., Cavagna, A., Cisbani, E., Giardina, I., Lecomte, V., Orlandi, A., Parisi, G., Procaccini, A., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0711437105 · Cited by 2,012
Abnormal 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
Efficient and Robust Feature Selection via Joint ℓ2,1-Norms Minimization
Nie, F., Huang, H., Cai, X., & Ding, C. H. Q. (2010) · Cited by 1,522
Self-citationCo-regularized Multi-view Spectral Clustering
Kumar, A., Rai, P., & Daumé, H. (2011) · Cited by 1,093
Clustering and projected clustering with adaptive neighbors
Nie, F., Wang, X., & Huang, H. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623726 · Cited by 981
Self-citationThe Constrained Laplacian Rank Algorithm for Graph-Based Clustering
Nie, F., Wang, X., Jordan, M., & Huang, H. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10302 · Cited by 837
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Cited by
Learning From Synthetic Data for Crowd Counting in the Wild
Wang, Q., Gao, J., Lin, W., & Yuan, Y. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00839 · Cited by 654
Self-citationWang, P., Sun, X., Diao, W., & Fu, K. (2020) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2954328 · Cited by 283
Metadata
- Authors
- Wang, Q., Chen, M., Nie, F., & Li, X.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2020-01-01
- Volume
- 42
- Issue
- 1
- Pages
- 46-58
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W2897819140
- Cited by
- 299
- References
- 43
- FWCI
- 24.92
- Type
- journal article
- Language
- en
- Open access
- closed
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- Wang, Q., Chen, M., Nie, F., & Li, X. (2020). Detecting Coherent Groups in Crowd Scenes by Multiview Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence. https://doi.org/10.1109/tpami.2018.2875002
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